From 11e1b0a20c07edf4b8fb8c1d073de2ad9b5a7eeb Mon Sep 17 00:00:00 2001 From: wenddymacro <50739376+wenddymacro@users.noreply.github.com> Date: Wed, 18 Mar 2026 14:04:03 +0800 Subject: [PATCH 01/19] Add WooldridgeDiD (ETWFE) design spec Design spec for integrating Stata jwdid (Wooldridge 2021/2023 ETWFE) into diff-diff as a standalone WooldridgeDiD estimator with linear and nonlinear support. Co-Authored-By: Claude Sonnet 4.6 --- .../specs/2026-03-18-wooldridge-did-design.md | 265 ++++++++++++++++++ 1 file changed, 265 insertions(+) create mode 100644 docs/superpowers/specs/2026-03-18-wooldridge-did-design.md diff --git a/docs/superpowers/specs/2026-03-18-wooldridge-did-design.md b/docs/superpowers/specs/2026-03-18-wooldridge-did-design.md new file mode 100644 index 00000000..954771cf --- /dev/null +++ b/docs/superpowers/specs/2026-03-18-wooldridge-did-design.md @@ -0,0 +1,265 @@ +# WooldridgeDiD Estimator — Design Spec + +**Date:** 2026-03-18 +**Status:** Approved +**Scope:** Integrate Stata `jwdid` (Wooldridge ETWFE) functionality into diff-diff + +--- + +## 1. Background and Motivation + +The Stata package `jwdid` (Friosavila 2021) implements Wooldridge's (2021, 2023) Extended +Two-Way Fixed Effects (ETWFE) estimator for staggered DiD. Its key advantages over existing +diff-diff estimators are: + +- **Saturated regression**: estimates all cohort×time ATT(g,t) in a single pooled OLS, + more efficient than Callaway-Sant'Anna's pair-wise approach +- **Nonlinear extension**: Wooldridge (2023) extends ETWFE to logit and Poisson, avoiding + the incidental parameters problem — no other estimator in diff-diff supports this +- **Equivalence to CS**: under identical assumptions, ETWFE ATT(g,t) equals CS ATT(g,t) + +**Primary references:** +- Wooldridge (2021). "Two-Way Fixed Effects, the Two-Way Mundlak Regression, and + Difference-in-Differences Estimators." SSRN 3906345. +- Wooldridge (2023). "Simple approaches to nonlinear difference-in-differences with panel + data." *The Econometrics Journal*, 26(3), C31–C66. +- Friosavila (2021). `jwdid`: Stata module. SSC s459114. + +--- + +## 2. Architecture Overview + +### New files +| File | Purpose | +|------|---------| +| `diff_diff/wooldridge.py` | `WooldridgeDiD` estimator class | +| `diff_diff/wooldridge_results.py` | `WooldridgeDiDResults` dataclass | +| `tests/test_wooldridge.py` | Full test suite | + +### Modified files +| File | Change | +|------|--------| +| `diff_diff/__init__.py` | Export `WooldridgeDiD`, `WooldridgeDiDResults` | +| `docs/methodology/REGISTRY.md` | Add ETWFE methodology section | + +### Class hierarchy +`WooldridgeDiD` is a **standalone estimator** (same level as `CallawaySantAnna`, +`SunAbraham`, etc.), not inheriting from `DifferenceInDifferences`. It implements its own +`get_params` / `set_params`. + +--- + +## 3. Public API + +### Constructor + +```python +class WooldridgeDiD: + def __init__( + self, + method: str = "ols", # "ols" | "logit" | "poisson" + control_group: str = "not_yet_treated", # "never_treated" | "not_yet_treated" + anticipation: int = 0, # pre-treatment periods to include + demean_covariates: bool = True, # within cohort-period demeaning (jwdid default) + alpha: float = 0.05, + cluster: Optional[str] = None, # default: unit identifier (jwdid default) + n_bootstrap: int = 0, # >0 enables multiplier bootstrap + bootstrap_weights: str = "rademacher", # "rademacher" | "webb" | "mammen" + seed: Optional[int] = None, + rank_deficient_action: str = "warn", # "warn" | "error" | "silent" + ): ... +``` + +### fit() + +```python +def fit( + self, + data: pd.DataFrame, + outcome: str, + unit: str, + time: str, + cohort: str, # first treatment period; 0/NaN = never treated + exovar: Optional[List[str]] = None, # time-invariant covariates (no interaction) + xtvar: Optional[List[str]] = None, # time-varying covariates (demeaned within cohort-period) + xgvar: Optional[List[str]] = None, # cohort-interacted covariates +) -> "WooldridgeDiDResults": ... +``` + +**Notes:** +- `cohort` column convention: integer = first treatment period, 0 or NaN = never treated. + Consistent with `CallawaySantAnna`'s `cohort` parameter. +- Default clustering is at the `unit` level (matches `jwdid` default of `vce(cluster ivar)`). +- `demean_covariates=True` corresponds to `jwdid` default; `False` corresponds to `xasis` option. + +### get_params / set_params + +```python +def get_params(self) -> Dict[str, Any]: ... # returns all constructor params +def set_params(self, **params) -> "WooldridgeDiD": ... # sklearn-compatible +``` + +--- + +## 4. Results Object + +```python +@dataclass +class WooldridgeDiDResults: + # Raw cohort×time estimates — core output + group_time_effects: Dict[Tuple[Any, Any], Dict[str, Any]] + # key = (g, t); value = {"att", "se", "t_stat", "p_value", "conf_int"} + + # Simple aggregation (always computed on fit) + overall_att: float + overall_se: float + overall_t_stat: float + overall_p_value: float + overall_conf_int: Tuple[float, float] + + # Other aggregations (populated by .aggregate()) + group_effects: Optional[Dict[Any, Dict]] # keyed by cohort g + calendar_effects: Optional[Dict[Any, Dict]] # keyed by calendar period t + event_study_effects: Optional[Dict[int, Dict]] # keyed by relative period k = t - g + + # Metadata + method: str + control_group: str + groups: List[Any] + time_periods: List[Any] + n_obs: int + n_treated_units: int + n_control_units: int + alpha: float = 0.05 + + # Methods + def aggregate(self, type: str) -> "WooldridgeDiDResults": ... + # type: "simple" | "group" | "calendar" | "event" + # fills corresponding fields, returns self for chaining + + def summary(self, aggregation: str = "simple") -> str: ... + def to_dataframe(self, aggregation: str = "event") -> pd.DataFrame: ... + def plot_event_study(self, **kwargs) -> None: ... + def __repr__(self) -> str: ... +``` + +**Inference rule:** ALL inference fields (t_stat, p_value, conf_int) computed together +via `safe_inference()` from `diff_diff.utils`. Never computed individually. + +--- + +## 5. Internal Computation + +### 5a. Linear ETWFE (`method="ols"`) + +Faithful port of `jwdid` + `reghdfe`: + +1. **Filter observations**: keep control group (never- or not-yet-treated at time t) plus + all treated units. Drop observations where `t < g - anticipation`. + +2. **Build interaction matrix**: for each (g, t) with `t >= g - anticipation`, create + column `1(G_i = g) * 1(T = t)`. These are the β_{g,t} regressors. + +3. **Covariate preparation**: + - `exovar`: append as-is + - `xtvar`: demean within (cohort × period) cells when `demean_covariates=True` + - `xgvar`: interact with each cohort indicator + +4. **Absorb unit + time FE**: within-transformation (existing `absorb` mechanism in + `linalg.py`), not explicit dummies. + +5. **Solve**: `linalg.solve_ols()` → extract β_{g,t} coefficients and vcov submatrix. + +6. **Inference**: `linalg.compute_robust_vcov()` with unit-level clustering by default, + then `safe_inference()` for each (g, t) cell. + +7. **Bootstrap**: multiplier bootstrap supported for all inference; + wild cluster bootstrap supported for linear only (same as `DifferenceInDifferences`). + +### 5b. Nonlinear (`method="logit"|"poisson"`) + +Following Wooldridge (2023) pooled QMLE approach: + +- **Logit**: group-level FE (cohort × period), **not** individual FE — avoids incidental + parameters problem. Log-likelihood: Bernoulli QLL. +- **Poisson**: individual FE absorbed via PPML (iterative within-transformation). + Log-likelihood: Poisson QLL. + +Optimization: `scipy.optimize.minimize` (L-BFGS-B). Vcov from numerical Hessian +(`scipy.optimize.approx_fprime` second differences). + +**ATT computation via Average Structural Function (ASF):** +Coefficients on treatment interactions are not directly ATTs. Must compute: +``` +ATT(g,t) = mean[ g(X_i'β̂ + δ̂_{g,t}) - g(X_i'β̂) ] over treated units in (g,t) +``` +where `g(·)` = logistic or exp. Delta method for SE propagation. + +Bootstrap: multiplier bootstrap only (no wild cluster bootstrap for nonlinear). + +### 5c. Aggregation Weights (exact jwdid_estat formula) + +``` +ω(g,t) = number of unit-time observations in cell (g,t) + +simple: Σ_{g,t: t≥g} ω(g,t)·ATT(g,t) / Σ_{g,t: t≥g} ω(g,t) +group: Σ_{t≥g} ω(g,t)·ATT(g,t) / Σ_{t≥g} ω(g,t) ∀g +calendar: Σ_{g: t≥g} ω(g,t)·ATT(g,t) / Σ_{g: t≥g} ω(g,t) ∀t +event: Σ_g ω(g,g+k)·ATT(g,g+k) / Σ_g ω(g,g+k) ∀k +``` + +Aggregation SEs: delta method for linear (variance of weighted sum); bootstrap +distribution used when `n_bootstrap > 0`. + +--- + +## 6. Parallel Trends Assumptions + +| `control_group` | Assumption | Pre-treatment effects | +|-----------------|------------|----------------------| +| `"not_yet_treated"` (default) | Parallel trends between each cohort and not-yet-treated units | Constrained to zero by design | +| `"never_treated"` | Parallel trends between each cohort and never-treated units | Estimable (visible in event study k < 0) | + +--- + +## 7. Testing Strategy + +### test_wooldridge.py structure + +**API tests** +- Invalid `method` / `control_group` raises `ValueError` +- `get_params()` / `set_params()` round-trip +- Accessing `results_` before `fit()` raises + +**Basic functionality** +- Fit on `mpdta` dataset, all fields non-NaN (`assert_nan_inference()`) +- All four aggregations callable and produce sensible output +- `to_dataframe()` and `summary()` run without error + +**Methodology correctness** +- Linear ETWFE ATT(g,t) ≈ CallawaySantAnna ATT(g,t) on same data / same control group + (tolerance ~1e-3, both theoretically equivalent under OLS / same assumptions) +- Nonlinear: simulated binary data, logit ATT sign correct +- Aggregation weight verification: manual weighted average == `simple` ATT + +**Edge cases** +- `control_group="never_treated"` with pre-treatment k < 0 effects estimable +- `anticipation=1` shifts treatment window correctly +- All three covariate types passed simultaneously +- Single cohort degenerates to standard DiD + +**Slow tests** (`@pytest.mark.slow`) +- Bootstrap SE convergence (`ci_params.bootstrap(n, min_n=199)`, threshold 0.40/0.15) +- Nonlinear bootstrap + +--- + +## 8. Documentation + +- `docs/methodology/REGISTRY.md`: add "WooldridgeDiD / ETWFE" section with: + - Academic sources (Wooldridge 2021, 2023; Friosavila 2021) + - Estimator equation (saturated model) + - SE methods (unit-level cluster, multiplier bootstrap, wild cluster bootstrap for OLS) + - Edge cases: nonlinear ASF computation, covariate demeaning + - Note: `**Deviation from Stata:** nonlinear bootstrap uses multiplier (jwdid uses delta method)` +- Export as `WooldridgeDiD` and alias `ETWFE` in `__init__.py` From bfcbf001b37674754dea2f558bf6e7d847c88417 Mon Sep 17 00:00:00 2001 From: wenddymacro <50739376+wenddymacro@users.noreply.github.com> Date: Wed, 18 Mar 2026 17:41:31 +0800 Subject: [PATCH 02/19] Revise WooldridgeDiD spec: fix P1/P2 issues from review MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Specify correct within_transform location (diff_diff.utils, not linalg) - Fix logit FE: explicit drop_first dummies to avoid solve_logit intercept collision - Add complete solve_poisson() spec with signature, clipping, convergence behavior - Add full delta method gradient vector for nonlinear ASF SEs - Fix REGISTRY deviation label to use recognized "- **Note:**" format - Clarify control-group observation filter for anticipation - Fix "constrained to zero" → "not estimated (cells excluded)" - Add _demeaned suffix tracking note for within_transform - Align ETWFE alias across Sections 2 and 8 Co-Authored-By: Claude Sonnet 4.6 --- .../specs/2026-03-18-wooldridge-did-design.md | 100 +++++++++++++----- 1 file changed, 71 insertions(+), 29 deletions(-) diff --git a/docs/superpowers/specs/2026-03-18-wooldridge-did-design.md b/docs/superpowers/specs/2026-03-18-wooldridge-did-design.md index 954771cf..6b60176b 100644 --- a/docs/superpowers/specs/2026-03-18-wooldridge-did-design.md +++ b/docs/superpowers/specs/2026-03-18-wooldridge-did-design.md @@ -39,7 +39,7 @@ diff-diff estimators are: ### Modified files | File | Change | |------|--------| -| `diff_diff/__init__.py` | Export `WooldridgeDiD`, `WooldridgeDiDResults` | +| `diff_diff/__init__.py` | Export `WooldridgeDiD`, `WooldridgeDiDResults`; alias `ETWFE = WooldridgeDiD` | | `docs/methodology/REGISTRY.md` | Add ETWFE methodology section | ### Class hierarchy @@ -91,6 +91,8 @@ def fit( Consistent with `CallawaySantAnna`'s `cohort` parameter. - Default clustering is at the `unit` level (matches `jwdid` default of `vce(cluster ivar)`). - `demean_covariates=True` corresponds to `jwdid` default; `False` corresponds to `xasis` option. +- When `demean_covariates=False`, `xtvar` variables are treated identically to `exovar` + (appended without demeaning or interaction). ### get_params / set_params @@ -140,6 +142,7 @@ class WooldridgeDiDResults: def summary(self, aggregation: str = "simple") -> str: ... def to_dataframe(self, aggregation: str = "event") -> pd.DataFrame: ... def plot_event_study(self, **kwargs) -> None: ... + # delegates to diff_diff.visualization plot utilities (same pattern as CallawaySantAnna) def __repr__(self) -> str: ... ``` @@ -154,46 +157,77 @@ via `safe_inference()` from `diff_diff.utils`. Never computed individually. Faithful port of `jwdid` + `reghdfe`: -1. **Filter observations**: keep control group (never- or not-yet-treated at time t) plus - all treated units. Drop observations where `t < g - anticipation`. +1. **Filter observations**: + - Treated units: keep all observations. + - `not_yet_treated` control: at each time t, include units with cohort > t (not yet + treated). Drop treated units' observations where `t < g - anticipation` (pre-treatment + beyond anticipation window). Control observations are kept for all t. + - `never_treated` control: include only units with cohort = 0 or NaN. Drop treated + unit observations where `t < g - anticipation`. 2. **Build interaction matrix**: for each (g, t) with `t >= g - anticipation`, create - column `1(G_i = g) * 1(T = t)`. These are the β_{g,t} regressors. + column `1(G_i = g) * 1(T = t)`. These are the β_{g,t} regressors. Cells with + `t < g - anticipation` are excluded from the model (not constrained — simply absent). 3. **Covariate preparation**: - - `exovar`: append as-is - - `xtvar`: demean within (cohort × period) cells when `demean_covariates=True` - - `xgvar`: interact with each cohort indicator + - `exovar`: append as-is (no interaction, no demeaning) + - `xtvar`: when `demean_covariates=True`, demean within (cohort × period) cells using + pandas `groupby([cohort_col, time]).transform("mean")`; when `demean_covariates=False`, + append as-is (equivalent to `xasis` option in jwdid) + - `xgvar`: interact each variable with each cohort indicator column -4. **Absorb unit + time FE**: within-transformation (existing `absorb` mechanism in - `linalg.py`), not explicit dummies. +4. **Absorb unit + time FE**: two-way within-transformation using + `within_transform(data, variables, unit, time, suffix="_demeaned")` from `diff_diff.utils`. + This performs iterative demeaning equivalent to unit + time dummy absorption. Applied to + outcome and all regressors before `solve_ols`. Track demeaned columns via the `_demeaned` + suffix (e.g., `outcome` → `outcome_demeaned`) when slicing the DataFrame for `solve_ols`. -5. **Solve**: `linalg.solve_ols()` → extract β_{g,t} coefficients and vcov submatrix. +5. **Solve**: `linalg.solve_ols()` on the within-transformed design matrix → extract + β_{g,t} coefficients and full vcov matrix. -6. **Inference**: `linalg.compute_robust_vcov()` with unit-level clustering by default, - then `safe_inference()` for each (g, t) cell. +6. **Inference**: `linalg.compute_robust_vcov()` with unit-level clustering by default + (pass `cluster=unit` column), then `safe_inference()` for each (g, t) cell. 7. **Bootstrap**: multiplier bootstrap supported for all inference; wild cluster bootstrap supported for linear only (same as `DifferenceInDifferences`). ### 5b. Nonlinear (`method="logit"|"poisson"`) -Following Wooldridge (2023) pooled QMLE approach: - -- **Logit**: group-level FE (cohort × period), **not** individual FE — avoids incidental - parameters problem. Log-likelihood: Bernoulli QLL. -- **Poisson**: individual FE absorbed via PPML (iterative within-transformation). - Log-likelihood: Poisson QLL. - -Optimization: `scipy.optimize.minimize` (L-BFGS-B). Vcov from numerical Hessian -(`scipy.optimize.approx_fprime` second differences). +Following Wooldridge (2023) pooled QMLE approach. Both methods use **explicit cohort×period +group FE** (not individual FE) to avoid the incidental parameters problem: + +- **Logit**: Bernoulli QLL. Use existing `linalg.solve_logit()` (IRLS). Build design matrix + with explicit cohort×period group FE dummies using `pd.get_dummies(..., drop_first=False)`, + then **drop one dummy column** (reference category) before passing to `solve_logit`. + `solve_logit` prepends its own intercept internally; providing dummies that span the + constant without dropping one causes rank deficiency and silent coefficient dropping. + Dropping one cohort×period category is the correct fix (standard dummy variable trap). + +- **Poisson**: Poisson QLL. Implement `linalg.solve_poisson()` — a new function with + signature `solve_poisson(X, y, max_iter=25, tol=1e-8) -> Tuple[ndarray, ndarray]` + (mirrors `solve_logit` signature). Uses Poisson IRLS (Newton-Raphson with log link): + - Initialize: `β = zeros`; `μ̂ = clip(exp(Xβ), 1e-10, None)` (clip prevents log(0)) + - Weight update: `W = diag(μ̂)` + - Newton step: `β ← β + (X'WX)^{-1} X'(y - μ̂)` (score / Hessian) + - Convergence: `‖β_new - β_old‖_∞ < tol`; warn if `max_iter` reached without convergence + - Returns `(β, W_final)` where `W_final` is used by caller for vcov + - Does **not** prepend intercept automatically (caller includes intercept or FE dummies) + - Also uses explicit cohort×period group FE dummies with one dropped (same as logit) + +Vcov for both: sandwich estimator `(X'WX)^{-1} (X'Ûû'X) (X'WX)^{-1}` (robust/clustered), +where Û contains Pearson residuals `(y - μ̂)`. Mirrors `linalg.compute_robust_vcov` pattern. **ATT computation via Average Structural Function (ASF):** -Coefficients on treatment interactions are not directly ATTs. Must compute: +Coefficients on treatment interactions are not directly ATTs. Compute: ``` -ATT(g,t) = mean[ g(X_i'β̂ + δ̂_{g,t}) - g(X_i'β̂) ] over treated units in (g,t) +ATT(g,t) = mean[ g(η_i + δ̂_{g,t}) - g(η_i) ] over treated units in (g,t) ``` -where `g(·)` = logistic or exp. Delta method for SE propagation. +where `η_i = X_i'β̂` is the baseline linear index and `g(·)` = logistic or exp. + +SE via full delta method. Define gradient vector `∇θ ∈ ℝ^K` (one entry per coefficient): +- For `δ_{g,t}`: `∂ATT/∂δ_{g,t} = mean[ g'(η_i + δ̂_{g,t}) ]` +- For any baseline covariate `β_k`: `∂ATT/∂β_k = mean[ x_{ik} (g'(η_i + δ̂_{g,t}) - g'(η_i)) ]` +Then `Var(ATT(g,t)) = ∇θ' Σ_β ∇θ` using the full parameter vcov matrix. Bootstrap: multiplier bootstrap only (no wild cluster bootstrap for nonlinear). @@ -208,8 +242,14 @@ calendar: Σ_{g: t≥g} ω(g,t)·ATT(g,t) / Σ_{g: t≥g} ω(g,t) ∀ event: Σ_g ω(g,g+k)·ATT(g,g+k) / Σ_g ω(g,g+k) ∀k ``` -Aggregation SEs: delta method for linear (variance of weighted sum); bootstrap -distribution used when `n_bootstrap > 0`. +**Aggregation SEs (delta method):** For a weighted aggregate `θ̄ = Σ w_{g,t} β_{g,t}` +where weights are treated as fixed: +``` +Var(θ̄) = w' Σ_β w +``` +where `w` is the weight vector and `Σ_β` is the full vcov submatrix of all β_{g,t} +coefficients (extracted from `solve_ols` vcov). This requires storing the full β vcov, +not just diagonal SEs. When `n_bootstrap > 0`, use bootstrap distribution instead. --- @@ -217,7 +257,7 @@ distribution used when `n_bootstrap > 0`. | `control_group` | Assumption | Pre-treatment effects | |-----------------|------------|----------------------| -| `"not_yet_treated"` (default) | Parallel trends between each cohort and not-yet-treated units | Constrained to zero by design | +| `"not_yet_treated"` (default) | Parallel trends between each cohort and not-yet-treated units | Not estimated — cells with `t < g` are excluded from the model | | `"never_treated"` | Parallel trends between each cohort and never-treated units | Estimable (visible in event study k < 0) | --- @@ -238,7 +278,9 @@ distribution used when `n_bootstrap > 0`. **Methodology correctness** - Linear ETWFE ATT(g,t) ≈ CallawaySantAnna ATT(g,t) on same data / same control group - (tolerance ~1e-3, both theoretically equivalent under OLS / same assumptions) + (tolerance ~1e-2 relative, both theoretically equivalent under OLS / same assumptions; + exact equality holds asymptotically but finite-sample differences exist due to + different control group construction details) - Nonlinear: simulated binary data, logit ATT sign correct - Aggregation weight verification: manual weighted average == `simple` ATT @@ -261,5 +303,5 @@ distribution used when `n_bootstrap > 0`. - Estimator equation (saturated model) - SE methods (unit-level cluster, multiplier bootstrap, wild cluster bootstrap for OLS) - Edge cases: nonlinear ASF computation, covariate demeaning - - Note: `**Deviation from Stata:** nonlinear bootstrap uses multiplier (jwdid uses delta method)` + - `- **Note:** nonlinear bootstrap uses multiplier bootstrap; jwdid uses delta method` - Export as `WooldridgeDiD` and alias `ETWFE` in `__init__.py` From 5b30d0a42ffb8881a44d33c6d3aa7ea99aa0db87 Mon Sep 17 00:00:00 2001 From: wenddymacro <50739376+wenddymacro@users.noreply.github.com> Date: Wed, 18 Mar 2026 17:54:39 +0800 Subject: [PATCH 03/19] chore: save WooldridgeDiD implementation plan and progress checkpoint --- docs/superpowers/CHECKPOINT.md | 131 + .../plans/2026-03-18-wooldridge-did.md | 2233 +++++++++++++++++ 2 files changed, 2364 insertions(+) create mode 100644 docs/superpowers/CHECKPOINT.md create mode 100644 docs/superpowers/plans/2026-03-18-wooldridge-did.md diff --git a/docs/superpowers/CHECKPOINT.md b/docs/superpowers/CHECKPOINT.md new file mode 100644 index 00000000..ba069b2d --- /dev/null +++ b/docs/superpowers/CHECKPOINT.md @@ -0,0 +1,131 @@ +# WooldridgeDiD 实现进度存档 + +**最后更新：** 2026-03-18 +**当前阶段：** 设计完成，实现规划完成，待修复计划文档 P1/P2 问题后开始编码 + +--- + +## 任务背景 + +将 Stata 包 `jwdid`（Wooldridge 2021/2023 Extended Two-Way Fixed Effects）整合到 diff-diff Python 库，保持与 Stata 实现一致。 + +--- + +## 已完成工作 + +### 设计阶段 ✅ +- **设计文档**：`docs/superpowers/specs/2026-03-18-wooldridge-did-design.md` + - 经两轮 spec review，所有 P1/P2 问题已解决，已 approved + - 主要决定：单类 `WooldridgeDiD`（别名 `ETWFE`），`method` 参数切换 ols/logit/poisson + +### 实现规划阶段 ✅（待修复） +- **实现计划**：`docs/superpowers/plans/2026-03-18-wooldridge-did.md` + - 12 个任务，TDD 风格 + - plan review 发现 **3 个 P1 问题** + **6 个 P2 问题**，**尚未修复**，下次开始前需先修复 + +--- + +## 下次开始时的步骤 + +### 第一步：修复实现计划中的 P1/P2 问题 + +在编写任何代码之前，先修复 `docs/superpowers/plans/2026-03-18-wooldridge-did.md`： + +#### P1 问题（必须修复） + +**P1-1：Task 7，`_fit_logit` 中 `compute_robust_vcov` 被传了不存在的 `weights=` 参数** +- 位置：Task 7 Step 3，`_fit_logit` 方法 +- 问题：`compute_robust_vcov(X, resids, cluster_ids=..., weights=probs*(1-probs))` — 该函数无 `weights` 参数，会报 `TypeError` +- 修复：手动计算 logit 加权 sandwich vcov，方式与 `_fit_poisson` 中相同： + ```python + W = probs * (1 - probs) # logit variance + XtWX = X_with_intercept.T @ (W[:, None] * X_with_intercept) + XtWX_inv = np.linalg.inv(XtWX) + resids = y - probs + # cluster or plain meat + meat = ... # 同 _fit_poisson 的聚类 meat 计算 + vcov_full = XtWX_inv @ meat @ XtWX_inv + ``` + +**P1-2：Task 9，bootstrap 块引用了未定义的变量 `post_keys`** +- 位置：Task 9 Step 3，`_fit_ols` bootstrap 块 +- 修复：在 bootstrap 循环前加一行： + ```python + post_keys = [(g, t) for (g, t) in gt_keys if t >= g] + ``` + +**P1-3：Task 7，logit delta method 梯度向量计算有 bug** +- 位置：Task 7 Step 3，`_fit_logit` 的 ATT delta method 部分 +- 问题：`grad += np.mean(d_base, axis=0)` 会覆盖掉之前设置的 `grad[1 + idx] = d_delta` +- 修复：分开设置，不相互覆盖： + ```python + grad = np.mean(d_base, axis=0).copy() # baseline covariate partials + grad[1 + idx] += d_delta # add treatment coefficient partial + ``` + +#### P2 问题（建议修复） + +**P2-1：Task 4，`_filter_sample` 中 `not_yet_treated` 分支有死代码** +- 删除第一个 `control_mask = ...` 赋值（立即被覆盖） + +**P2-2：Task 4，`_build_interaction_matrix` 对 `not_yet_treated` 包含了不应包含的 pre-treatment cells** +- 当 `anticipation==0` 时，`not_yet_treated` 控制组下不应包含 `t < g` 的格子 +- 需要在调用 `_build_interaction_matrix` 时传入 `control_group` 参数并据此过滤 + +**P2-3：Task 6 缺少 TDD 红色阶段** +- 在 Task 6 Step 2 中补上"先运行确认失败"的说明 + +**P2-4：Task 2，`_make_minimal_results` 缺少 `_gt_keys` 字段** +- 在测试 helper 中加入 `_gt_keys=[(2,2),(2,3),(3,3)]` + +**P2-5：Task 5，缺少解释为何不加截距的注释** +- 在 `_fit_ols` 的 `solve_ols` 调用处加注释说明 within-transform 后不需要截距 + +**P2-6：Task 10，缺少 CS（Callaway-Sant'Anna）等价性测试** +- Spec 第 7 节要求：线性 ETWFE 的 ATT(g,t) 应约等于 CallawaySantAnna 的 ATT(g,t)（容差 ~1e-2） +- 需在 Task 10 中加该对比测试 + +### 第二步：修复完毕后开始实现 + +按计划文档 Task 1 → Task 12 顺序执行，使用 `superpowers:subagent-driven-development` 或 `superpowers:executing-plans`。 + +--- + +## 关键文件位置 + +| 文件 | 说明 | +|------|------| +| `docs/superpowers/specs/2026-03-18-wooldridge-did-design.md` | 设计文档（已 approved） | +| `docs/superpowers/plans/2026-03-18-wooldridge-did.md` | 实现计划（需先修复 P1/P2） | +| `docs/superpowers/CHECKPOINT.md` | 本文件 | + +## 将新建的文件 + +| 文件 | 说明 | +|------|------| +| `diff_diff/wooldridge.py` | `WooldridgeDiD` 估计器主体 | +| `diff_diff/wooldridge_results.py` | `WooldridgeDiDResults` 结果类 | +| `tests/test_wooldridge.py` | 完整测试套件 | + +## 将修改的文件 + +| 文件 | 修改内容 | +|------|---------| +| `diff_diff/linalg.py` | 新增 `solve_poisson()` IRLS 求解器 | +| `diff_diff/__init__.py` | 导出 `WooldridgeDiD`、`WooldridgeDiDResults`、别名 `ETWFE` | +| `docs/methodology/REGISTRY.md` | 新增 ETWFE 方法论章节 | + +--- + +## 设计摘要（供快速回顾） + +| 方面 | 决定 | +|------|------| +| 类名 | `WooldridgeDiD`，别名 `ETWFE` | +| 线性（`method="ols"`） | 饱和 cohort×time 交互 + `within_transform` 吸收 unit/time FE → `solve_ols` | +| Logit（`method="logit"`） | 显式 cohort×period group FE dummies（drop_first）+ `solve_logit` + ASF ATT | +| Poisson（`method="poisson"`） | 新 `solve_poisson` IRLS + group FE dummies + ASF ATT | +| 控制组 | `not_yet_treated`（默认）或 `never_treated`，通过参数切换 | +| 聚合 | 4 种：simple / group / calendar / event，精确对应 `jwdid_estat` 权重公式 | +| 标准误 | 默认 unit 层面聚类（= jwdid 默认）；可选 multiplier bootstrap + wild cluster bootstrap（仅 OLS）| +| 协变量 | `exovar`（不交互）/ `xtvar`（cohort×period 去均值）/ `xgvar`（与队列交互）| diff --git a/docs/superpowers/plans/2026-03-18-wooldridge-did.md b/docs/superpowers/plans/2026-03-18-wooldridge-did.md new file mode 100644 index 00000000..728ff137 --- /dev/null +++ b/docs/superpowers/plans/2026-03-18-wooldridge-did.md @@ -0,0 +1,2233 @@ +# WooldridgeDiD (ETWFE) Implementation Plan + +> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking. + +**Goal:** Implement `WooldridgeDiD` (Wooldridge 2021/2023 Extended Two-Way Fixed Effects) as a standalone estimator in diff-diff, faithful to the Stata `jwdid` package. + +**Architecture:** Standalone estimator class (not inheriting `DifferenceInDifferences`) with separate results dataclass. Linear path uses existing `solve_ols` + `within_transform`; nonlinear path adds a new `solve_poisson` to `linalg.py` and leverages existing `solve_logit`. All four `jwdid_estat` aggregation types supported. Bootstrap via existing multiplier and wild-cluster mechanisms. + +**Tech Stack:** numpy, pandas, scipy (no new dependencies). Existing `linalg.solve_ols`, `linalg.solve_logit`, `utils.within_transform`, `utils.safe_inference`. + +**Spec:** `docs/superpowers/specs/2026-03-18-wooldridge-did-design.md` + +--- + +## File Map + +| Action | File | Responsibility | +|--------|------|----------------| +| Create | `diff_diff/wooldridge_results.py` | `WooldridgeDiDResults` dataclass + aggregation methods | +| Create | `diff_diff/wooldridge.py` | `WooldridgeDiD` estimator: constructor, fit (OLS + nonlinear), bootstrap | +| Create | `tests/test_wooldridge.py` | Full test suite | +| Modify | `diff_diff/linalg.py` | Add `solve_poisson()` IRLS solver | +| Modify | `diff_diff/__init__.py` | Export `WooldridgeDiD`, `WooldridgeDiDResults`, alias `ETWFE` | +| Modify | `docs/methodology/REGISTRY.md` | Add ETWFE methodology section | + +--- + +## Task 1: `solve_poisson()` in `linalg.py` + +**Files:** +- Modify: `diff_diff/linalg.py` (append after `solve_logit`) +- Test: `tests/test_linalg.py` (add to existing file) + +- [ ] **Step 1: Write the failing test** + +Open `tests/test_linalg.py` and add at the end: + +```python +class TestSolvePoisson: + def test_basic_convergence(self): + """solve_poisson converges on simple count data.""" + rng = np.random.default_rng(42) + n = 200 + X = np.column_stack([np.ones(n), rng.standard_normal((n, 2))]) + true_beta = np.array([0.5, 0.3, -0.2]) + mu = np.exp(X @ true_beta) + y = rng.poisson(mu).astype(float) + beta, W = solve_poisson(X, y) + assert beta.shape == (3,) + assert W.shape == (n,) + assert np.allclose(beta, true_beta, atol=0.15) + + def test_returns_weights(self): + """solve_poisson returns final mu weights for vcov computation.""" + rng = np.random.default_rng(0) + n = 100 + X = np.column_stack([np.ones(n), rng.standard_normal(n)]) + y = rng.poisson(2.0, size=n).astype(float) + beta, W = solve_poisson(X, y) + assert (W > 0).all() + + def test_non_negative_output(self): + """Fitted mu = exp(Xb) should be strictly positive.""" + rng = np.random.default_rng(1) + n = 50 + X = np.column_stack([np.ones(n), rng.standard_normal(n)]) + y = rng.poisson(1.0, size=n).astype(float) + beta, W = solve_poisson(X, y) + mu_hat = np.exp(X @ beta) + assert (mu_hat > 0).all() + + def test_no_intercept_prepended(self): + """solve_poisson does NOT add intercept (caller's responsibility).""" + rng = np.random.default_rng(2) + n = 80 + # X already has intercept — verify coefficient count matches columns + X = np.column_stack([np.ones(n), rng.standard_normal(n)]) + y = rng.poisson(1.5, size=n).astype(float) + beta, _ = solve_poisson(X, y) + assert len(beta) == 2 # not 3 +``` + +Add import at top of test file: `from diff_diff.linalg import solve_poisson` + +- [ ] **Step 2: Run test to verify it fails** + +```bash +pytest tests/test_linalg.py::TestSolvePoisson -v +``` + +Expected: `ImportError: cannot import name 'solve_poisson'` + +- [ ] **Step 3: Implement `solve_poisson` in `linalg.py`** + +Append after the `solve_logit` function (around line 960): + +```python +def solve_poisson( + X: np.ndarray, + y: np.ndarray, + max_iter: int = 25, + tol: float = 1e-8, +) -> Tuple[np.ndarray, np.ndarray]: + """Poisson IRLS (Newton-Raphson with log link). + + Does NOT prepend an intercept — caller must include one if needed. + Returns (beta, W_final) where W_final = mu_hat (used for sandwich vcov). + + Parameters + ---------- + X : (n, k) design matrix (caller provides intercept / group FE dummies) + y : (n,) non-negative count outcomes + max_iter : maximum IRLS iterations + tol : convergence threshold on sup-norm of coefficient change + + Returns + ------- + beta : (k,) coefficient vector + W : (n,) final fitted means mu_hat (weights for sandwich vcov) + """ + n, k = X.shape + beta = np.zeros(k) + for _ in range(max_iter): + eta = X @ beta + mu = np.clip(np.exp(eta), 1e-10, None) # clip prevents log(0) + score = X.T @ (y - mu) # gradient of log-likelihood + hess = X.T @ (mu[:, None] * X) # -Hessian = X'WX, W=diag(mu) + try: + delta = np.linalg.solve(hess, score) + except np.linalg.LinAlgError: + break + beta_new = beta + delta + if np.max(np.abs(beta_new - beta)) < tol: + beta = beta_new + mu = np.clip(np.exp(X @ beta), 1e-10, None) + break + beta = beta_new + else: + import warnings + warnings.warn( + "solve_poisson did not converge in {} iterations".format(max_iter), + RuntimeWarning, + stacklevel=2, + ) + mu_final = np.clip(np.exp(X @ beta), 1e-10, None) + return beta, mu_final +``` + +- [ ] **Step 4: Run tests to verify they pass** + +```bash +pytest tests/test_linalg.py::TestSolvePoisson -v +``` + +Expected: all 4 tests PASS + +- [ ] **Step 5: Commit** + +```bash +git add diff_diff/linalg.py tests/test_linalg.py +git commit -m "feat(linalg): add solve_poisson IRLS solver for Wooldridge nonlinear ETWFE" +``` + +--- + +## Task 2: `WooldridgeDiDResults` dataclass + +**Files:** +- Create: `diff_diff/wooldridge_results.py` +- Test: `tests/test_wooldridge.py` (create file, results section) + +- [ ] **Step 1: Write the failing tests** + +Create `tests/test_wooldridge.py`: + +```python +"""Tests for WooldridgeDiD estimator and WooldridgeDiDResults.""" +import numpy as np +import pandas as pd +import pytest +from diff_diff.wooldridge_results import WooldridgeDiDResults + + +def _make_minimal_results(**kwargs): + """Helper: build a WooldridgeDiDResults with required fields.""" + defaults = dict( + group_time_effects={ + (2, 2): {"att": 1.0, "se": 0.5, "t_stat": 2.0, "p_value": 0.04, "conf_int": (0.02, 1.98)}, + (2, 3): {"att": 1.5, "se": 0.6, "t_stat": 2.5, "p_value": 0.01, "conf_int": (0.32, 2.68)}, + (3, 3): {"att": 0.8, "se": 0.4, "t_stat": 2.0, "p_value": 0.04, "conf_int": (0.02, 1.58)}, + }, + overall_att=1.1, + overall_se=0.35, + overall_t_stat=3.14, + overall_p_value=0.002, + overall_conf_int=(0.41, 1.79), + group_effects=None, + calendar_effects=None, + event_study_effects=None, + method="ols", + control_group="not_yet_treated", + groups=[2, 3], + time_periods=[1, 2, 3], + n_obs=300, + n_treated_units=100, + n_control_units=200, + alpha=0.05, + _gt_weights={(2, 2): 50, (2, 3): 50, (3, 3): 30}, + _gt_vcov=None, + ) + defaults.update(kwargs) + return WooldridgeDiDResults(**defaults) + + +class TestWooldridgeDiDResults: + def test_repr(self): + r = _make_minimal_results() + s = repr(r) + assert "WooldridgeDiDResults" in s + assert "ATT" in s + + def test_summary_default(self): + r = _make_minimal_results() + s = r.summary() + assert "1.1" in s or "ATT" in s + + def test_to_dataframe_event(self): + r = _make_minimal_results() + r.aggregate("event") + df = r.to_dataframe("event") + assert isinstance(df, pd.DataFrame) + assert "att" in df.columns + + def test_aggregate_simple_returns_self(self): + r = _make_minimal_results() + result = r.aggregate("simple") + assert result is r # chaining + + def test_aggregate_group(self): + r = _make_minimal_results() + r.aggregate("group") + assert r.group_effects is not None + assert 2 in r.group_effects + assert 3 in r.group_effects + + def test_aggregate_calendar(self): + r = _make_minimal_results() + r.aggregate("calendar") + assert r.calendar_effects is not None + assert 2 in r.calendar_effects or 3 in r.calendar_effects + + def test_aggregate_event(self): + r = _make_minimal_results() + r.aggregate("event") + assert r.event_study_effects is not None + # relative period 0 (treatment period itself) should be present + assert 0 in r.event_study_effects or 1 in r.event_study_effects + + def test_aggregate_invalid_raises(self): + r = _make_minimal_results() + with pytest.raises(ValueError, match="type"): + r.aggregate("bad_type") +``` + +- [ ] **Step 2: Run tests to verify they fail** + +```bash +pytest tests/test_wooldridge.py::TestWooldridgeDiDResults -v +``` + +Expected: `ModuleNotFoundError: No module named 'diff_diff.wooldridge_results'` + +- [ ] **Step 3: Implement `wooldridge_results.py`** + +Create `diff_diff/wooldridge_results.py`: + +```python +"""Results class for WooldridgeDiD (ETWFE) estimator.""" +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any, Dict, List, Optional, Tuple + +import numpy as np +import pandas as pd + +from diff_diff.utils import safe_inference + + +@dataclass +class WooldridgeDiDResults: + """Results from WooldridgeDiD.fit(). + + Core output is ``group_time_effects``: a dict keyed by (cohort_g, time_t) + with per-cell ATT estimates and inference. Call ``.aggregate(type)`` to + compute any of the four jwdid_estat aggregation types. + """ + + # ------------------------------------------------------------------ # + # Core cohort×time estimates # + # ------------------------------------------------------------------ # + group_time_effects: Dict[Tuple[Any, Any], Dict[str, Any]] + """key=(g,t), value={att, se, t_stat, p_value, conf_int}""" + + # ------------------------------------------------------------------ # + # Simple (overall) aggregation — always populated at fit time # + # ------------------------------------------------------------------ # + overall_att: float + overall_se: float + overall_t_stat: float + overall_p_value: float + overall_conf_int: Tuple[float, float] + + # ------------------------------------------------------------------ # + # Other aggregations — populated by .aggregate() # + # ------------------------------------------------------------------ # + group_effects: Optional[Dict[Any, Dict]] = field(default=None, repr=False) + calendar_effects: Optional[Dict[Any, Dict]] = field(default=None, repr=False) + event_study_effects: Optional[Dict[int, Dict]] = field(default=None, repr=False) + + # ------------------------------------------------------------------ # + # Metadata # + # ------------------------------------------------------------------ # + method: str = "ols" + control_group: str = "not_yet_treated" + groups: List[Any] = field(default_factory=list) + time_periods: List[Any] = field(default_factory=list) + n_obs: int = 0 + n_treated_units: int = 0 + n_control_units: int = 0 + alpha: float = 0.05 + + # ------------------------------------------------------------------ # + # Internal — used by aggregate() for delta-method SEs # + # ------------------------------------------------------------------ # + _gt_weights: Dict[Tuple[Any, Any], int] = field(default_factory=dict, repr=False) + _gt_vcov: Optional[np.ndarray] = field(default=None, repr=False) + """Full vcov of all β_{g,t} coefficients (ordered same as sorted group_time_effects keys).""" + _gt_keys: List[Tuple[Any, Any]] = field(default_factory=list, repr=False) + """Ordered list of (g,t) keys corresponding to _gt_vcov columns.""" + + # ------------------------------------------------------------------ # + # Public methods # + # ------------------------------------------------------------------ # + + def aggregate(self, type: str) -> "WooldridgeDiDResults": # noqa: A002 + """Compute and store one of the four jwdid_estat aggregation types. + + Parameters + ---------- + type : "simple" | "group" | "calendar" | "event" + + Returns self for chaining. + """ + valid = ("simple", "group", "calendar", "event") + if type not in valid: + raise ValueError(f"type must be one of {valid}, got {type!r}") + + gt = self.group_time_effects + weights = self._gt_weights + vcov = self._gt_vcov + keys_ordered = self._gt_keys if self._gt_keys else sorted(gt.keys()) + + def _agg_se(w_vec: np.ndarray) -> float: + """Delta-method SE for a linear combination w'β given full vcov.""" + if vcov is None or len(w_vec) != vcov.shape[0]: + return float("nan") + return float(np.sqrt(max(w_vec @ vcov @ w_vec, 0.0))) + + def _build_effect(att: float, se: float) -> Dict[str, Any]: + t_stat, p_value, conf_int = safe_inference(att, se, alpha=self.alpha) + return {"att": att, "se": se, "t_stat": t_stat, + "p_value": p_value, "conf_int": conf_int} + + if type == "simple": + # Re-compute overall using delta method (already stored in overall_* fields) + # This is a no-op but keeps the method callable. + pass + + elif type == "group": + result: Dict[Any, Dict] = {} + for g in self.groups: + cells = [(g2, t) for (g2, t) in keys_ordered if g2 == g and t >= g] + if not cells: + continue + w_total = sum(weights.get(c, 0) for c in cells) + if w_total == 0: + continue + att = sum(weights.get(c, 0) * gt[c]["att"] for c in cells) / w_total + # delta-method weights vector over all keys_ordered + w_vec = np.array([ + weights.get(c, 0) / w_total if c in cells else 0.0 + for c in keys_ordered + ]) + se = _agg_se(w_vec) + result[g] = _build_effect(att, se) + self.group_effects = result + + elif type == "calendar": + result = {} + for t in self.time_periods: + cells = [(g, t2) for (g, t2) in keys_ordered if t2 == t and t >= g] + if not cells: + continue + w_total = sum(weights.get(c, 0) for c in cells) + if w_total == 0: + continue + att = sum(weights.get(c, 0) * gt[c]["att"] for c in cells) / w_total + w_vec = np.array([ + weights.get(c, 0) / w_total if c in cells else 0.0 + for c in keys_ordered + ]) + se = _agg_se(w_vec) + result[t] = _build_effect(att, se) + self.calendar_effects = result + + elif type == "event": + all_k = sorted({t - g for (g, t) in keys_ordered}) + result = {} + for k in all_k: + cells = [(g, t) for (g, t) in keys_ordered if t - g == k] + if not cells: + continue + w_total = sum(weights.get(c, 0) for c in cells) + if w_total == 0: + continue + att = sum(weights.get(c, 0) * gt[c]["att"] for c in cells) / w_total + w_vec = np.array([ + weights.get(c, 0) / w_total if c in cells else 0.0 + for c in keys_ordered + ]) + se = _agg_se(w_vec) + result[k] = _build_effect(att, se) + self.event_study_effects = result + + return self + + def summary(self, aggregation: str = "simple") -> str: + """Print formatted summary table. + + Parameters + ---------- + aggregation : which aggregation to display ("simple", "group", "calendar", "event") + """ + lines = [ + "=" * 70, + " Wooldridge Extended Two-Way Fixed Effects (ETWFE) Results", + "=" * 70, + f"Method: {self.method}", + f"Control group: {self.control_group}", + f"Observations: {self.n_obs}", + f"Treated units: {self.n_treated_units}", + f"Control units: {self.n_control_units}", + "-" * 70, + ] + + def _fmt_row(label: str, att: float, se: float, t: float, + p: float, ci: Tuple) -> str: + from diff_diff.results import _get_significance_stars # type: ignore + stars = _get_significance_stars(p) if not np.isnan(p) else "" + ci_lo = f"{ci[0]:.4f}" if not np.isnan(ci[0]) else "NaN" + ci_hi = f"{ci[1]:.4f}" if not np.isnan(ci[1]) else "NaN" + return ( + f"{label:<22} {att:>10.4f} {se:>10.4f} {t:>8.3f} " + f"{p:>8.4f}{stars} [{ci_lo}, {ci_hi}]" + ) + + header = ( + f"{'Parameter':<22} {'Estimate':>10} {'Std. Err.':>10} " + f"{'t-stat':>8} {'P>|t|':>8} [95% CI]" + ) + lines.append(header) + lines.append("-" * 70) + + if aggregation == "simple": + lines.append(_fmt_row( + "ATT (simple)", + self.overall_att, self.overall_se, + self.overall_t_stat, self.overall_p_value, self.overall_conf_int, + )) + elif aggregation == "group" and self.group_effects: + for g, eff in sorted(self.group_effects.items()): + lines.append(_fmt_row( + f"ATT(g={g})", + eff["att"], eff["se"], eff["t_stat"], eff["p_value"], eff["conf_int"], + )) + elif aggregation == "calendar" and self.calendar_effects: + for t, eff in sorted(self.calendar_effects.items()): + lines.append(_fmt_row( + f"ATT(t={t})", + eff["att"], eff["se"], eff["t_stat"], eff["p_value"], eff["conf_int"], + )) + elif aggregation == "event" and self.event_study_effects: + for k, eff in sorted(self.event_study_effects.items()): + label = f"ATT(k={k})" + (" [pre]" if k < 0 else "") + lines.append(_fmt_row( + label, eff["att"], eff["se"], + eff["t_stat"], eff["p_value"], eff["conf_int"], + )) + else: + lines.append(f" (call .aggregate({aggregation!r}) first)") + + lines.append("=" * 70) + return "\n".join(lines) + + def to_dataframe(self, aggregation: str = "event") -> pd.DataFrame: + """Export aggregated effects to a DataFrame. + + Parameters + ---------- + aggregation : "simple" | "group" | "calendar" | "event" | "gt" + Use "gt" to export raw group-time effects. + """ + if aggregation == "gt": + rows = [] + for (g, t), eff in sorted(self.group_time_effects.items()): + row = {"cohort": g, "time": t, "relative_period": t - g} + row.update(eff) + rows.append(row) + return pd.DataFrame(rows) + + mapping = { + "simple": [{"label": "ATT", "att": self.overall_att, + "se": self.overall_se, "t_stat": self.overall_t_stat, + "p_value": self.overall_p_value, + "conf_int_lo": self.overall_conf_int[0], + "conf_int_hi": self.overall_conf_int[1]}], + "group": [ + {"cohort": g, **{k: v for k, v in eff.items() if k != "conf_int"}, + "conf_int_lo": eff["conf_int"][0], "conf_int_hi": eff["conf_int"][1]} + for g, eff in sorted((self.group_effects or {}).items()) + ], + "calendar": [ + {"time": t, **{k: v for k, v in eff.items() if k != "conf_int"}, + "conf_int_lo": eff["conf_int"][0], "conf_int_hi": eff["conf_int"][1]} + for t, eff in sorted((self.calendar_effects or {}).items()) + ], + "event": [ + {"relative_period": k, + **{kk: vv for kk, vv in eff.items() if kk != "conf_int"}, + "conf_int_lo": eff["conf_int"][0], "conf_int_hi": eff["conf_int"][1]} + for k, eff in sorted((self.event_study_effects or {}).items()) + ], + } + rows = mapping.get(aggregation, []) + return pd.DataFrame(rows) + + def plot_event_study(self, **kwargs) -> None: + """Event study plot. Calls aggregate('event') if needed.""" + if self.event_study_effects is None: + self.aggregate("event") + from diff_diff.visualization import plot_event_study # type: ignore + effects = {k: v["att"] for k, v in (self.event_study_effects or {}).items()} + se = {k: v["se"] for k, v in (self.event_study_effects or {}).items()} + plot_event_study(effects=effects, se=se, alpha=self.alpha, **kwargs) + + def __repr__(self) -> str: + n_gt = len(self.group_time_effects) + att_str = f"{self.overall_att:.4f}" if not np.isnan(self.overall_att) else "NaN" + se_str = f"{self.overall_se:.4f}" if not np.isnan(self.overall_se) else "NaN" + p_str = f"{self.overall_p_value:.4f}" if not np.isnan(self.overall_p_value) else "NaN" + return ( + f"WooldridgeDiDResults(" + f"ATT={att_str}, SE={se_str}, p={p_str}, " + f"n_gt={n_gt}, method={self.method!r})" + ) +``` + +- [ ] **Step 4: Run tests to verify they pass** + +```bash +pytest tests/test_wooldridge.py::TestWooldridgeDiDResults -v +``` + +Expected: all 8 tests PASS + +- [ ] **Step 5: Commit** + +```bash +git add diff_diff/wooldridge_results.py tests/test_wooldridge.py +git commit -m "feat: add WooldridgeDiDResults dataclass with four aggregation types" +``` + +--- + +## Task 3: `WooldridgeDiD` constructor, `get_params`, `set_params` + +**Files:** +- Create: `diff_diff/wooldridge.py` +- Test: `tests/test_wooldridge.py` (add API tests) + +- [ ] **Step 1: Write the failing tests** + +Add to `tests/test_wooldridge.py`: + +```python +from diff_diff.wooldridge import WooldridgeDiD + + +class TestWooldridgeDiDAPI: + def test_default_construction(self): + est = WooldridgeDiD() + assert est.method == "ols" + assert est.control_group == "not_yet_treated" + assert est.anticipation == 0 + assert est.demean_covariates is True + assert est.alpha == 0.05 + assert est.cluster is None + assert est.n_bootstrap == 0 + assert est.bootstrap_weights == "rademacher" + assert est.seed is None + assert est.rank_deficient_action == "warn" + assert not est.is_fitted_ + + def test_invalid_method_raises(self): + with pytest.raises(ValueError, match="method"): + WooldridgeDiD(method="probit") + + def test_invalid_control_group_raises(self): + with pytest.raises(ValueError, match="control_group"): + WooldridgeDiD(control_group="clean_control") + + def test_invalid_anticipation_raises(self): + with pytest.raises(ValueError, match="anticipation"): + WooldridgeDiD(anticipation=-1) + + def test_get_params_roundtrip(self): + est = WooldridgeDiD(method="logit", alpha=0.1, anticipation=1) + params = est.get_params() + assert params["method"] == "logit" + assert params["alpha"] == 0.1 + assert params["anticipation"] == 1 + + def test_set_params_roundtrip(self): + est = WooldridgeDiD() + est.set_params(alpha=0.01, n_bootstrap=100) + assert est.alpha == 0.01 + assert est.n_bootstrap == 100 + + def test_set_params_returns_self(self): + est = WooldridgeDiD() + result = est.set_params(alpha=0.1) + assert result is est + + def test_set_params_unknown_raises(self): + est = WooldridgeDiD() + with pytest.raises(ValueError, match="Unknown"): + est.set_params(nonexistent_param=42) + + def test_results_before_fit_raises(self): + est = WooldridgeDiD() + with pytest.raises(RuntimeError, match="fit"): + _ = est.results_ +``` + +- [ ] **Step 2: Run tests to verify they fail** + +```bash +pytest tests/test_wooldridge.py::TestWooldridgeDiDAPI -v +``` + +Expected: `ModuleNotFoundError: No module named 'diff_diff.wooldridge'` + +- [ ] **Step 3: Implement constructor + get/set params** + +Create `diff_diff/wooldridge.py`: + +```python +"""WooldridgeDiD: Extended Two-Way Fixed Effects (ETWFE) estimator. + +Implements Wooldridge (2021, 2023) ETWFE, faithful to the Stata jwdid package. + +References +---------- +Wooldridge (2021). Two-Way Fixed Effects, the Two-Way Mundlak Regression, + and Difference-in-Differences Estimators. SSRN 3906345. +Wooldridge (2023). Simple approaches to nonlinear difference-in-differences + with panel data. The Econometrics Journal, 26(3), C31-C66. +Friosavila (2021). jwdid: Stata module. SSC s459114. +""" +from __future__ import annotations + +from typing import Any, Dict, List, Optional, Tuple + +import numpy as np +import pandas as pd + +from diff_diff.linalg import compute_robust_vcov, solve_logit, solve_ols, solve_poisson +from diff_diff.utils import safe_inference, within_transform +from diff_diff.wooldridge_results import WooldridgeDiDResults + +_VALID_METHODS = ("ols", "logit", "poisson") +_VALID_CONTROL_GROUPS = ("never_treated", "not_yet_treated") +_VALID_BOOTSTRAP_WEIGHTS = ("rademacher", "webb", "mammen") + + +class WooldridgeDiD: + """Extended Two-Way Fixed Effects (ETWFE) DiD estimator. + + Implements the Wooldridge (2021) saturated cohort×time regression and + Wooldridge (2023) nonlinear extensions (logit, Poisson). Produces all + four ``jwdid_estat`` aggregation types: simple, group, calendar, event. + + Parameters + ---------- + method : {"ols", "logit", "poisson"} + Estimation method. "ols" for continuous outcomes; "logit" for binary + or fractional outcomes; "poisson" for count data. + control_group : {"not_yet_treated", "never_treated"} + Which units serve as the comparison group. "not_yet_treated" (jwdid + default) uses all untreated observations at each time period; + "never_treated" uses only units never treated throughout the sample. + anticipation : int + Number of periods before treatment onset to include as treatment cells + (anticipation effects). 0 means no anticipation. + demean_covariates : bool + If True (jwdid default), ``xtvar`` covariates are demeaned within each + cohort×period cell before entering the regression. Set to False to + replicate jwdid's ``xasis`` option. + alpha : float + Significance level for confidence intervals. + cluster : str or None + Column name to use for cluster-robust SEs. Defaults to the ``unit`` + identifier passed to ``fit()``. + n_bootstrap : int + Number of bootstrap replications. 0 disables bootstrap. + bootstrap_weights : {"rademacher", "webb", "mammen"} + Bootstrap weight distribution. + seed : int or None + Random seed for reproducibility. + rank_deficient_action : {"warn", "error", "silent"} + How to handle rank-deficient design matrices. + """ + + def __init__( + self, + method: str = "ols", + control_group: str = "not_yet_treated", + anticipation: int = 0, + demean_covariates: bool = True, + alpha: float = 0.05, + cluster: Optional[str] = None, + n_bootstrap: int = 0, + bootstrap_weights: str = "rademacher", + seed: Optional[int] = None, + rank_deficient_action: str = "warn", + ) -> None: + if method not in _VALID_METHODS: + raise ValueError(f"method must be one of {_VALID_METHODS}, got {method!r}") + if control_group not in _VALID_CONTROL_GROUPS: + raise ValueError( + f"control_group must be one of {_VALID_CONTROL_GROUPS}, got {control_group!r}" + ) + if anticipation < 0: + raise ValueError(f"anticipation must be >= 0, got {anticipation}") + + self.method = method + self.control_group = control_group + self.anticipation = anticipation + self.demean_covariates = demean_covariates + self.alpha = alpha + self.cluster = cluster + self.n_bootstrap = n_bootstrap + self.bootstrap_weights = bootstrap_weights + self.seed = seed + self.rank_deficient_action = rank_deficient_action + + self.is_fitted_: bool = False + self._results: Optional[WooldridgeDiDResults] = None + + @property + def results_(self) -> WooldridgeDiDResults: + if not self.is_fitted_: + raise RuntimeError("Call fit() before accessing results_") + return self._results # type: ignore[return-value] + + def get_params(self) -> Dict[str, Any]: + """Return estimator parameters (sklearn-compatible).""" + return { + "method": self.method, + "control_group": self.control_group, + "anticipation": self.anticipation, + "demean_covariates": self.demean_covariates, + "alpha": self.alpha, + "cluster": self.cluster, + "n_bootstrap": self.n_bootstrap, + "bootstrap_weights": self.bootstrap_weights, + "seed": self.seed, + "rank_deficient_action": self.rank_deficient_action, + } + + def set_params(self, **params: Any) -> "WooldridgeDiD": + """Set estimator parameters (sklearn-compatible). Returns self.""" + for key, value in params.items(): + if not hasattr(self, key): + raise ValueError(f"Unknown parameter: {key!r}") + setattr(self, key, value) + return self + + def fit( + self, + data: pd.DataFrame, + outcome: str, + unit: str, + time: str, + cohort: str, + exovar: Optional[List[str]] = None, + xtvar: Optional[List[str]] = None, + xgvar: Optional[List[str]] = None, + ) -> WooldridgeDiDResults: + """Fit the ETWFE model. See class docstring for parameter details. + + Parameters + ---------- + data : DataFrame with panel data (long format) + outcome : outcome column name + unit : unit identifier column + time : time period column + cohort : first treatment period (0 or NaN = never treated) + exovar : time-invariant covariates added without interaction/demeaning + xtvar : time-varying covariates (demeaned within cohort×period cells + when ``demean_covariates=True``) + xgvar : covariates interacted with each cohort indicator + """ + # Placeholder — implementation in Tasks 4 & 5 + raise NotImplementedError("fit() implemented in later tasks") +``` + +- [ ] **Step 4: Run tests to verify they pass** + +```bash +pytest tests/test_wooldridge.py::TestWooldridgeDiDAPI -v +``` + +Expected: all 9 tests PASS + +- [ ] **Step 5: Commit** + +```bash +git add diff_diff/wooldridge.py tests/test_wooldridge.py +git commit -m "feat: add WooldridgeDiD class scaffold with constructor and param API" +``` + +--- + +## Task 4: Data preparation helpers (filter, interaction matrix, covariate prep) + +**Files:** +- Modify: `diff_diff/wooldridge.py` (add private helpers) +- Test: `tests/test_wooldridge.py` (add internal prep tests) + +- [ ] **Step 1: Write the failing tests** + +Add to `tests/test_wooldridge.py`: + +```python +from diff_diff.wooldridge import ( + _filter_sample, + _build_interaction_matrix, + _prepare_covariates, +) + + +def _make_panel(n_units=10, n_periods=5, treat_share=0.5, seed=0): + """Create a simple balanced panel for testing.""" + rng = np.random.default_rng(seed) + units = np.arange(n_units) + n_treated = int(n_units * treat_share) + # Two cohorts: half treated in period 3, rest never treated + cohort = np.array([3] * n_treated + [0] * (n_units - n_treated)) + rows = [] + for u in units: + for t in range(1, n_periods + 1): + rows.append({"unit": u, "time": t, "cohort": cohort[u], + "y": rng.standard_normal(), "x1": rng.standard_normal()}) + return pd.DataFrame(rows) + + +class TestDataPrep: + def test_filter_sample_not_yet_treated(self): + df = _make_panel() + filtered = _filter_sample(df, unit="unit", time="time", cohort="cohort", + control_group="not_yet_treated", anticipation=0) + # All treated units should be present (all periods) + treated_units = df[df["cohort"] == 3]["unit"].unique() + assert set(treated_units).issubset(filtered["unit"].unique()) + + def test_filter_sample_never_treated(self): + df = _make_panel() + filtered = _filter_sample(df, unit="unit", time="time", cohort="cohort", + control_group="never_treated", anticipation=0) + # Only never-treated (cohort==0) and treated units should remain + # No not-yet-treated-only units; here all non-treated have cohort==0 + assert (filtered["cohort"].isin([0, 3])).all() + + def test_build_interaction_matrix_columns(self): + df = _make_panel() + filtered = _filter_sample(df, "unit", "time", "cohort", + "not_yet_treated", anticipation=0) + X_int, col_names, gt_keys = _build_interaction_matrix( + filtered, cohort="cohort", time="time", anticipation=0 + ) + # Each column should be a valid (g, t) pair with t >= g + for (g, t) in gt_keys: + assert t >= g + + def test_build_interaction_matrix_binary(self): + df = _make_panel() + filtered = _filter_sample(df, "unit", "time", "cohort", + "not_yet_treated", anticipation=0) + X_int, col_names, gt_keys = _build_interaction_matrix( + filtered, cohort="cohort", time="time", anticipation=0 + ) + # All values should be 0 or 1 + assert set(np.unique(X_int)).issubset({0, 1}) + + def test_prepare_covariates_exovar(self): + df = _make_panel() + X_cov = _prepare_covariates(df, exovar=["x1"], xtvar=None, xgvar=None, + cohort="cohort", time="time", + demean_covariates=True, groups=[3]) + assert X_cov.shape[0] == len(df) + assert X_cov.shape[1] == 1 # just x1 + + def test_prepare_covariates_xtvar_demeaned(self): + df = _make_panel() + X_raw = _prepare_covariates(df, exovar=None, xtvar=["x1"], xgvar=None, + cohort="cohort", time="time", + demean_covariates=False, groups=[3]) + X_dem = _prepare_covariates(df, exovar=None, xtvar=["x1"], xgvar=None, + cohort="cohort", time="time", + demean_covariates=True, groups=[3]) + # Demeaned version should differ from raw + assert not np.allclose(X_raw, X_dem) +``` + +- [ ] **Step 2: Run tests to verify they fail** + +```bash +pytest tests/test_wooldridge.py::TestDataPrep -v +``` + +Expected: `ImportError` (functions not yet defined) + +- [ ] **Step 3: Implement helper functions** + +Add to `diff_diff/wooldridge.py` (before the `WooldridgeDiD` class definition): + +```python +def _filter_sample( + data: pd.DataFrame, + unit: str, + time: str, + cohort: str, + control_group: str, + anticipation: int, +) -> pd.DataFrame: + """Return the analysis sample following jwdid selection rules. + + Treated units: all observations kept (pre-treatment window beyond + anticipation is not used as a treatment cell but is kept for FE). + Control units: for "not_yet_treated", units with cohort > t at each t + (including never-treated); for "never_treated", only cohort == 0/NaN. + """ + df = data.copy() + # Normalise never-treated: fill NaN cohort with 0 + df[cohort] = df[cohort].fillna(0) + + treated_mask = df[cohort] > 0 + + if control_group == "never_treated": + control_mask = df[cohort] == 0 + else: # not_yet_treated + # A unit is "not yet treated" at time t if its cohort > t + control_mask = (~treated_mask) | (df[cohort] > df[time]) + # Keep untreated-at-t observations for not-yet-treated units + control_mask = (df[cohort] == 0) | (df[cohort] > df[time]) + + return df[treated_mask | control_mask].copy() + + +def _build_interaction_matrix( + data: pd.DataFrame, + cohort: str, + time: str, + anticipation: int, +) -> Tuple[np.ndarray, List[str], List[Tuple[Any, Any]]]: + """Build the saturated cohort×time interaction design matrix. + + Returns + ------- + X_int : (n, n_cells) binary indicator matrix + col_names : list of string labels "g{g}_t{t}" + gt_keys : list of (g, t) tuples in same column order + """ + groups = sorted(g for g in data[cohort].unique() if g > 0) + times = sorted(data[time].unique()) + cohort_vals = data[cohort].values + time_vals = data[time].values + + cols = [] + col_names = [] + gt_keys = [] + + for g in groups: + for t in times: + if t >= g - anticipation: + indicator = ((cohort_vals == g) & (time_vals == t)).astype(float) + cols.append(indicator) + col_names.append(f"g{g}_t{t}") + gt_keys.append((g, t)) + + if not cols: + return np.empty((len(data), 0)), [], [] + return np.column_stack(cols), col_names, gt_keys + + +def _prepare_covariates( + data: pd.DataFrame, + exovar: Optional[List[str]], + xtvar: Optional[List[str]], + xgvar: Optional[List[str]], + cohort: str, + time: str, + demean_covariates: bool, + groups: List[Any], +) -> Optional[np.ndarray]: + """Build covariate matrix following jwdid covariate type conventions. + + Returns None if no covariates, else (n, k) array. + """ + parts = [] + + if exovar: + parts.append(data[exovar].values.astype(float)) + + if xtvar: + if demean_covariates: + # Within-cohort×period demeaning + grp_key = data[cohort].astype(str) + "_" + data[time].astype(str) + tmp = data[xtvar].copy() + for col in xtvar: + tmp[col] = tmp[col] - tmp.groupby(grp_key)[col].transform("mean") + parts.append(tmp.values.astype(float)) + else: + parts.append(data[xtvar].values.astype(float)) + + if xgvar: + for g in groups: + g_indicator = (data[cohort] == g).values.astype(float) + for col in xgvar: + parts.append((g_indicator * data[col].values).reshape(-1, 1)) + + if not parts: + return None + return np.hstack([p if p.ndim == 2 else p.reshape(-1, 1) for p in parts]) +``` + +Also update the imports at the top of the file to expose these as module-level functions +(they are already defined at module level, so they will be importable). + +- [ ] **Step 4: Run tests to verify they pass** + +```bash +pytest tests/test_wooldridge.py::TestDataPrep -v +``` + +Expected: all 6 tests PASS + +- [ ] **Step 5: Commit** + +```bash +git add diff_diff/wooldridge.py tests/test_wooldridge.py +git commit -m "feat: add ETWFE data preparation helpers (filter, interactions, covariates)" +``` + +--- + +## Task 5: Linear ETWFE `fit()` (OLS path) + +**Files:** +- Modify: `diff_diff/wooldridge.py` (implement `fit()` for method="ols") +- Test: `tests/test_wooldridge.py` (add fit tests) + +- [ ] **Step 1: Write the failing tests** + +Add to `tests/test_wooldridge.py`: + +```python +from diff_diff import load_dataset # or: from diff_diff.datasets import load_mpdta + + +class TestWooldridgeDiDFitOLS: + @pytest.fixture + def mpdta(self): + from diff_diff.datasets import load_mpdta + return load_mpdta() + + def test_fit_returns_results(self, mpdta): + est = WooldridgeDiD() + results = est.fit(mpdta, outcome="lemp", unit="countyreal", + time="year", cohort="first.treat") + assert isinstance(results, WooldridgeDiDResults) + + def test_fit_sets_is_fitted(self, mpdta): + est = WooldridgeDiD() + est.fit(mpdta, outcome="lemp", unit="countyreal", + time="year", cohort="first.treat") + assert est.is_fitted_ + + def test_overall_att_finite(self, mpdta): + est = WooldridgeDiD() + r = est.fit(mpdta, outcome="lemp", unit="countyreal", + time="year", cohort="first.treat") + assert np.isfinite(r.overall_att) + assert np.isfinite(r.overall_se) + assert r.overall_se > 0 + + def test_group_time_effects_populated(self, mpdta): + est = WooldridgeDiD() + r = est.fit(mpdta, outcome="lemp", unit="countyreal", + time="year", cohort="first.treat") + assert len(r.group_time_effects) > 0 + for (g, t), eff in r.group_time_effects.items(): + assert t >= g + assert "att" in eff and "se" in eff + + def test_all_inference_fields_finite(self, mpdta): + """No inference field should be NaN in normal data.""" + est = WooldridgeDiD() + r = est.fit(mpdta, outcome="lemp", unit="countyreal", + time="year", cohort="first.treat") + assert np.isfinite(r.overall_t_stat) + assert np.isfinite(r.overall_p_value) + assert all(np.isfinite(c) for c in r.overall_conf_int) + + def test_never_treated_control_group(self, mpdta): + est = WooldridgeDiD(control_group="never_treated") + r = est.fit(mpdta, outcome="lemp", unit="countyreal", + time="year", cohort="first.treat") + assert len(r.group_time_effects) > 0 + + def test_metadata_correct(self, mpdta): + est = WooldridgeDiD() + r = est.fit(mpdta, outcome="lemp", unit="countyreal", + time="year", cohort="first.treat") + assert r.method == "ols" + assert r.n_obs > 0 + assert r.n_treated_units > 0 + assert r.n_control_units > 0 +``` + +- [ ] **Step 2: Run tests to verify they fail** + +```bash +pytest tests/test_wooldridge.py::TestWooldridgeDiDFitOLS -v +``` + +Expected: `NotImplementedError: fit() implemented in later tasks` + +- [ ] **Step 3: Implement `fit()` OLS path** + +Replace the `fit()` placeholder in `diff_diff/wooldridge.py` with the full implementation. +Also add the `_fit_ols`, `_compute_gt_inference`, and `_build_simple_aggregation` helpers: + +```python +def fit( + self, + data: pd.DataFrame, + outcome: str, + unit: str, + time: str, + cohort: str, + exovar: Optional[List[str]] = None, + xtvar: Optional[List[str]] = None, + xgvar: Optional[List[str]] = None, +) -> WooldridgeDiDResults: + """Fit the ETWFE model.""" + df = data.copy() + df[cohort] = df[cohort].fillna(0) + + # 1. Filter to analysis sample + sample = _filter_sample(df, unit, time, cohort, self.control_group, self.anticipation) + + # 2. Build interaction matrix + X_int, col_names, gt_keys = _build_interaction_matrix( + sample, cohort=cohort, time=time, anticipation=self.anticipation + ) + + # 3. Covariates + groups = sorted(g for g in sample[cohort].unique() if g > 0) + X_cov = _prepare_covariates( + sample, exovar=exovar, xtvar=xtvar, xgvar=xgvar, + cohort=cohort, time=time, + demean_covariates=self.demean_covariates, + groups=groups, + ) + + all_regressors = col_names.copy() + if X_cov is not None: + X_design = np.hstack([X_int, X_cov]) + for i in range(X_cov.shape[1]): + all_regressors.append(f"_cov_{i}") + else: + X_design = X_int + + if self.method == "ols": + results = self._fit_ols( + sample, outcome, unit, time, cohort, + X_design, all_regressors, gt_keys, col_names, + groups, exovar, xtvar, xgvar, + ) + elif self.method == "logit": + results = self._fit_logit( + sample, outcome, unit, time, cohort, + X_design, all_regressors, gt_keys, col_names, groups, + ) + else: # poisson + results = self._fit_poisson( + sample, outcome, unit, time, cohort, + X_design, all_regressors, gt_keys, col_names, groups, + ) + + self._results = results + self.is_fitted_ = True + return results + + +def _fit_ols( + self, + sample: pd.DataFrame, + outcome: str, + unit: str, + time: str, + cohort: str, + X_design: np.ndarray, + col_names: List[str], + gt_keys: List[Tuple], + int_col_names: List[str], + groups: List[Any], + exovar, xtvar, xgvar, +) -> WooldridgeDiDResults: + """OLS path: within-transform FE, solve_ols, cluster SE.""" + n_int = len(int_col_names) # number of treatment interaction columns + + # 4. Within-transform: absorb unit + time FE + all_vars = [outcome] + [f"_x{i}" for i in range(X_design.shape[1])] + tmp = sample[[unit, time]].copy() + tmp[outcome] = sample[outcome].values + for i in range(X_design.shape[1]): + tmp[f"_x{i}"] = X_design[:, i] + + transformed = within_transform(tmp, all_vars, unit=unit, time=time, + suffix="_demeaned") + + y = transformed[f"{outcome}_demeaned"].values + X_cols = [f"_x{i}_demeaned" for i in range(X_design.shape[1])] + X = transformed[X_cols].values + + # 5. Cluster IDs (default: unit level) + cluster_col = self.cluster if self.cluster else unit + cluster_ids = sample[cluster_col].values + + # 6. Solve OLS + coefs, resids, vcov = solve_ols( + X, y, + cluster_ids=cluster_ids, + return_vcov=True, + rank_deficient_action=self.rank_deficient_action, + column_names=col_names, + ) + + # 7. Extract β_{g,t} and build gt_effects dict + gt_effects = {} + gt_weights = {} + for idx, (g, t) in enumerate(gt_keys): + if idx >= len(coefs): + break + att = float(coefs[idx]) + se = float(np.sqrt(vcov[idx, idx])) if vcov is not None else float("nan") + t_stat, p_value, conf_int = safe_inference(att, se, alpha=self.alpha) + gt_effects[(g, t)] = { + "att": att, "se": se, + "t_stat": t_stat, "p_value": p_value, "conf_int": conf_int, + } + gt_weights[(g, t)] = int(( + (sample[cohort] == g) & (sample[time] == t) + ).sum()) + + # Extract vcov submatrix for beta_{g,t} only + n_gt = len(gt_keys) + gt_vcov = vcov[:n_gt, :n_gt] if vcov is not None else None + gt_keys_ordered = list(gt_keys) + + # 8. Simple aggregation (always computed) + overall = _compute_weighted_agg(gt_effects, gt_weights, gt_keys_ordered, + gt_vcov, self.alpha) + + # Metadata + n_treated = int(sample[sample[cohort] > 0][unit].nunique()) + n_control = int(sample[sample[cohort] == 0][unit].nunique()) + all_times = sorted(sample[time].unique().tolist()) + + results = WooldridgeDiDResults( + group_time_effects=gt_effects, + overall_att=overall["att"], + overall_se=overall["se"], + overall_t_stat=overall["t_stat"], + overall_p_value=overall["p_value"], + overall_conf_int=overall["conf_int"], + method=self.method, + control_group=self.control_group, + groups=groups, + time_periods=all_times, + n_obs=len(sample), + n_treated_units=n_treated, + n_control_units=n_control, + alpha=self.alpha, + _gt_weights=gt_weights, + _gt_vcov=gt_vcov, + _gt_keys=gt_keys_ordered, + ) + return results + + +def _compute_weighted_agg( + gt_effects: Dict, + gt_weights: Dict, + gt_keys: List, + gt_vcov: Optional[np.ndarray], + alpha: float, +) -> Dict: + """Compute simple (overall) weighted average ATT and SE via delta method.""" + post_keys = [(g, t) for (g, t) in gt_keys if t >= g] + w_total = sum(gt_weights.get(k, 0) for k in post_keys) + if w_total == 0: + att = float("nan") + se = float("nan") + else: + att = sum(gt_weights.get(k, 0) * gt_effects[k]["att"] + for k in post_keys if k in gt_effects) / w_total + if gt_vcov is not None: + w_vec = np.array([ + gt_weights.get(k, 0) / w_total if k in post_keys else 0.0 + for k in gt_keys + ]) + var = float(w_vec @ gt_vcov @ w_vec) + se = float(np.sqrt(max(var, 0.0))) + else: + se = float("nan") + + t_stat, p_value, conf_int = safe_inference(att, se, alpha=alpha) + return {"att": att, "se": se, "t_stat": t_stat, + "p_value": p_value, "conf_int": conf_int} +``` + +Note: add `_fit_logit` and `_fit_poisson` stubs that raise `NotImplementedError` +(will be implemented in Task 7 & 8). + +- [ ] **Step 4: Run tests to verify they pass** + +```bash +pytest tests/test_wooldridge.py::TestWooldridgeDiDFitOLS -v +``` + +Expected: all 7 tests PASS + +- [ ] **Step 5: Commit** + +```bash +git add diff_diff/wooldridge.py tests/test_wooldridge.py +git commit -m "feat: implement WooldridgeDiD.fit() OLS path with ETWFE saturated regression" +``` + +--- + +## Task 6: Aggregation and output methods + +**Files:** +- Test: `tests/test_wooldridge.py` (add aggregation correctness tests) + +- [ ] **Step 1: Write the failing tests** + +Add to `tests/test_wooldridge.py`: + +```python +class TestAggregations: + @pytest.fixture + def fitted(self): + from diff_diff.datasets import load_mpdta + df = load_mpdta() + est = WooldridgeDiD() + return est.fit(df, outcome="lemp", unit="countyreal", + time="year", cohort="first.treat") + + def test_simple_matches_manual_weighted_average(self, fitted): + """simple ATT must equal manually computed weighted average of ATT(g,t).""" + gt = fitted.group_time_effects + w = fitted._gt_weights + post_keys = [(g, t) for (g, t) in w if t >= g] + w_total = sum(w[k] for k in post_keys) + manual_att = sum(w[k] * gt[k]["att"] for k in post_keys) / w_total + assert abs(fitted.overall_att - manual_att) < 1e-10 + + def test_aggregate_group_keys_match_cohorts(self, fitted): + fitted.aggregate("group") + assert set(fitted.group_effects.keys()) == set(fitted.groups) + + def test_aggregate_event_relative_periods(self, fitted): + fitted.aggregate("event") + for k in fitted.event_study_effects: + assert isinstance(k, (int, np.integer)) + + def test_aggregate_calendar_finite(self, fitted): + fitted.aggregate("calendar") + for t, eff in fitted.calendar_effects.items(): + assert np.isfinite(eff["att"]) + + def test_summary_runs(self, fitted): + s = fitted.summary("simple") + assert "ETWFE" in s or "Wooldridge" in s + + def test_to_dataframe_event(self, fitted): + fitted.aggregate("event") + df = fitted.to_dataframe("event") + assert "relative_period" in df.columns + assert "att" in df.columns + + def test_to_dataframe_gt(self, fitted): + df = fitted.to_dataframe("gt") + assert "cohort" in df.columns + assert "time" in df.columns + assert len(df) == len(fitted.group_time_effects) +``` + +- [ ] **Step 2: Run tests to verify they pass (most should already pass)** + +```bash +pytest tests/test_wooldridge.py::TestAggregations -v +``` + +Expected: all 7 tests PASS (aggregation logic is in `WooldridgeDiDResults`) + +- [ ] **Step 3: Commit if any fixes needed** + +If any tests reveal bugs in the aggregation code, fix and then: + +```bash +git add diff_diff/wooldridge_results.py diff_diff/wooldridge.py tests/test_wooldridge.py +git commit -m "fix: aggregation correctness and output method alignment" +``` + +--- + +## Task 7: Nonlinear fit — logit path + +**Files:** +- Modify: `diff_diff/wooldridge.py` (implement `_fit_logit`) +- Test: `tests/test_wooldridge.py` (add logit tests) + +- [ ] **Step 1: Write the failing tests** + +Add to `tests/test_wooldridge.py`: + +```python +class TestWooldridgeDiDLogit: + @pytest.fixture + def binary_panel(self): + """Simulated binary outcome panel with known positive ATT.""" + rng = np.random.default_rng(42) + n_units, n_periods = 60, 5 + rows = [] + for u in range(n_units): + cohort = 3 if u < 30 else 0 + for t in range(1, n_periods + 1): + treated = int(cohort > 0 and t >= cohort) + eta = -0.5 + 1.0 * treated + 0.1 * rng.standard_normal() + y = int(rng.random() < 1 / (1 + np.exp(-eta))) + rows.append({"unit": u, "time": t, "cohort": cohort, "y": y}) + return pd.DataFrame(rows) + + def test_logit_fit_runs(self, binary_panel): + est = WooldridgeDiD(method="logit") + r = est.fit(binary_panel, outcome="y", unit="unit", + time="time", cohort="cohort") + assert isinstance(r, WooldridgeDiDResults) + + def test_logit_att_sign(self, binary_panel): + """ATT should be positive (treatment increases binary outcome).""" + est = WooldridgeDiD(method="logit") + r = est.fit(binary_panel, outcome="y", unit="unit", + time="time", cohort="cohort") + assert r.overall_att > 0 + + def test_logit_se_positive(self, binary_panel): + est = WooldridgeDiD(method="logit") + r = est.fit(binary_panel, outcome="y", unit="unit", + time="time", cohort="cohort") + assert r.overall_se > 0 + + def test_logit_method_stored(self, binary_panel): + est = WooldridgeDiD(method="logit") + r = est.fit(binary_panel, outcome="y", unit="unit", + time="time", cohort="cohort") + assert r.method == "logit" +``` + +- [ ] **Step 2: Run tests to verify they fail** + +```bash +pytest tests/test_wooldridge.py::TestWooldridgeDiDLogit -v +``` + +Expected: `NotImplementedError` from `_fit_logit` stub + +- [ ] **Step 3: Implement `_fit_logit`** + +Add to `diff_diff/wooldridge.py` as a method of `WooldridgeDiD`: + +```python +def _fit_logit( + self, + sample: pd.DataFrame, + outcome: str, + unit: str, + time: str, + cohort: str, + X_int: np.ndarray, + col_names: List[str], + gt_keys: List[Tuple], + int_col_names: List[str], + groups: List[Any], +) -> WooldridgeDiDResults: + """Logit path: cohort×period group FE + solve_logit + ASF ATT.""" + n_int = len(int_col_names) + + # Build cohort×period group FE dummies (drop one to avoid collinearity + # with solve_logit's internal intercept) + grp_label = ( + sample[cohort].astype(str) + "_" + sample[time].astype(str) + ) + group_dummies = pd.get_dummies(grp_label, drop_first=True).values.astype(float) + + # Design matrix: treatment interactions + group FE dummies + X_full = np.hstack([X_int, group_dummies]) + + y = sample[outcome].values.astype(float) + cluster_col = self.cluster if self.cluster else unit + cluster_ids = sample[cluster_col].values + + beta, probs = solve_logit( + X_full, y, + rank_deficient_action=self.rank_deficient_action, + ) + # solve_logit prepends intercept — beta[0] is intercept, beta[1:] are X_full cols + beta_int_cols = beta[1: n_int + 1] # treatment interaction coefficients + + # Sandwich vcov for X_full (excluding intercept position 0) + resids = y - probs + X_with_intercept = np.column_stack([np.ones(len(y)), X_full]) + vcov_full = compute_robust_vcov( + X_with_intercept, resids, cluster_ids=cluster_ids, + weights=probs * (1 - probs), # logit variance weights + ) + # Submatrix for treatment interactions (skip intercept col 0) + vcov_int = vcov_full[1: n_int + 1, 1: n_int + 1] + + # ASF ATT(g,t) for treated units in each cell + gt_effects = {} + gt_weights = {} + for idx, (g, t) in enumerate(gt_keys): + if idx >= n_int: + break + cell_mask = (sample[cohort] == g) & (sample[time] == t) + if cell_mask.sum() == 0: + continue + # Baseline linear index for treated units in this cell + eta_base = X_with_intercept[cell_mask] @ beta # includes intercept + att = float(np.mean( + _logistic(eta_base + beta_int_cols[idx]) - _logistic(eta_base) + )) + # Delta method: full gradient over all K parameters (including intercept) + d_delta = np.mean( + _logistic_deriv(eta_base + beta_int_cols[idx]) + ) + d_base = X_with_intercept[cell_mask] * ( + _logistic_deriv(eta_base + beta_int_cols[idx]) - + _logistic_deriv(eta_base) + )[:, None] + grad = np.zeros(len(beta)) + grad[1 + idx] = d_delta + grad[1: n_int + 1] += np.zeros(n_int) # other deltas don't contribute + # base coefficient gradients + grad += np.mean(d_base, axis=0) + se = float(np.sqrt(max(grad @ vcov_full @ grad, 0.0))) + t_stat, p_value, conf_int = safe_inference(att, se, alpha=self.alpha) + gt_effects[(g, t)] = { + "att": att, "se": se, + "t_stat": t_stat, "p_value": p_value, "conf_int": conf_int, + } + gt_weights[(g, t)] = int(cell_mask.sum()) + + gt_keys_ordered = [k for k in gt_keys if k in gt_effects] + gt_vcov = None # full delta method used per-cell; aggregation uses None fallback + + overall = _compute_weighted_agg(gt_effects, gt_weights, gt_keys_ordered, + gt_vcov, self.alpha) + + return WooldridgeDiDResults( + group_time_effects=gt_effects, + overall_att=overall["att"], + overall_se=overall["se"], + overall_t_stat=overall["t_stat"], + overall_p_value=overall["p_value"], + overall_conf_int=overall["conf_int"], + method=self.method, + control_group=self.control_group, + groups=groups, + time_periods=sorted(sample[time].unique().tolist()), + n_obs=len(sample), + n_treated_units=int(sample[sample[cohort] > 0][unit].nunique()), + n_control_units=int(sample[sample[cohort] == 0][unit].nunique()), + alpha=self.alpha, + _gt_weights=gt_weights, + _gt_vcov=gt_vcov, + _gt_keys=gt_keys_ordered, + ) +``` + +Add helper functions at module level (before the class): + +```python +def _logistic(x: np.ndarray) -> np.ndarray: + return 1.0 / (1.0 + np.exp(-x)) + + +def _logistic_deriv(x: np.ndarray) -> np.ndarray: + p = _logistic(x) + return p * (1.0 - p) +``` + +- [ ] **Step 4: Run tests** + +```bash +pytest tests/test_wooldridge.py::TestWooldridgeDiDLogit -v +``` + +Expected: all 4 tests PASS + +- [ ] **Step 5: Commit** + +```bash +git add diff_diff/wooldridge.py tests/test_wooldridge.py +git commit -m "feat: implement WooldridgeDiD logit path with ASF ATT and delta-method SEs" +``` + +--- + +## Task 8: Nonlinear fit — Poisson path + +**Files:** +- Modify: `diff_diff/wooldridge.py` (implement `_fit_poisson`) +- Test: `tests/test_wooldridge.py` (add Poisson tests) + +- [ ] **Step 1: Write the failing tests** + +Add to `tests/test_wooldridge.py`: + +```python +class TestWooldridgeDiDPoisson: + @pytest.fixture + def count_panel(self): + rng = np.random.default_rng(7) + n_units, n_periods = 60, 5 + rows = [] + for u in range(n_units): + cohort = 3 if u < 30 else 0 + for t in range(1, n_periods + 1): + treated = int(cohort > 0 and t >= cohort) + mu = np.exp(0.5 + 0.8 * treated + 0.1 * rng.standard_normal()) + y = rng.poisson(mu) + rows.append({"unit": u, "time": t, "cohort": cohort, "y": float(y)}) + return pd.DataFrame(rows) + + def test_poisson_fit_runs(self, count_panel): + est = WooldridgeDiD(method="poisson") + r = est.fit(count_panel, outcome="y", unit="unit", + time="time", cohort="cohort") + assert isinstance(r, WooldridgeDiDResults) + + def test_poisson_att_sign(self, count_panel): + est = WooldridgeDiD(method="poisson") + r = est.fit(count_panel, outcome="y", unit="unit", + time="time", cohort="cohort") + assert r.overall_att > 0 + + def test_poisson_se_positive(self, count_panel): + est = WooldridgeDiD(method="poisson") + r = est.fit(count_panel, outcome="y", unit="unit", + time="time", cohort="cohort") + assert r.overall_se > 0 +``` + +- [ ] **Step 2: Run tests to verify they fail** + +```bash +pytest tests/test_wooldridge.py::TestWooldridgeDiDPoisson -v +``` + +Expected: `NotImplementedError` + +- [ ] **Step 3: Implement `_fit_poisson`** + +Add to `WooldridgeDiD` (mirrors `_fit_logit` but uses `solve_poisson` and exp link): + +```python +def _fit_poisson( + self, + sample: pd.DataFrame, + outcome: str, + unit: str, + time: str, + cohort: str, + X_int: np.ndarray, + col_names: List[str], + gt_keys: List[Tuple], + int_col_names: List[str], + groups: List[Any], +) -> WooldridgeDiDResults: + """Poisson path: cohort×period group FE + solve_poisson + ASF ATT.""" + n_int = len(int_col_names) + + # Group FE dummies (drop one reference category) + grp_label = ( + sample[cohort].astype(str) + "_" + sample[time].astype(str) + ) + group_dummies = pd.get_dummies(grp_label, drop_first=True).values.astype(float) + + # Design matrix: group FE dummies + treatment interactions + # Poisson solver does NOT prepend intercept; include group FE as baseline + X_full = np.hstack([group_dummies, X_int]) + n_fe = group_dummies.shape[1] + + y = sample[outcome].values.astype(float) + cluster_col = self.cluster if self.cluster else unit + cluster_ids = sample[cluster_col].values + + beta, mu_hat = solve_poisson(X_full, y) + + # Sandwich vcov: (X'WX)^{-1} (X'diag(resid^2)X) (X'WX)^{-1} + resids = y - mu_hat + W = mu_hat # Poisson variance = mean + XtWX = X_full.T @ (W[:, None] * X_full) + try: + XtWX_inv = np.linalg.inv(XtWX) + except np.linalg.LinAlgError: + XtWX_inv = np.full_like(XtWX, float("nan")) + + # Cluster-robust meat + if cluster_ids is not None: + clusters = np.unique(cluster_ids) + meat = np.zeros_like(XtWX) + for c in clusters: + mask = cluster_ids == c + scores_c = (X_full[mask] * resids[mask, None]).sum(axis=0) + meat += np.outer(scores_c, scores_c) + else: + scores = X_full * resids[:, None] + meat = scores.T @ scores + + vcov_full = XtWX_inv @ meat @ XtWX_inv + + # Interaction columns start at column n_fe in X_full + beta_int = beta[n_fe: n_fe + n_int] + vcov_int = vcov_full[n_fe: n_fe + n_int, n_fe: n_fe + n_int] + + # ASF ATT(g,t): E[exp(η + δ) - exp(η)] for treated units in cell + gt_effects = {} + gt_weights = {} + for idx, (g, t) in enumerate(gt_keys): + if idx >= n_int: + break + cell_mask = (sample[cohort] == g) & (sample[time] == t) + if cell_mask.sum() == 0: + continue + eta_base = X_full[cell_mask] @ beta + delta = beta_int[idx] + att = float(np.mean(np.exp(eta_base + delta) - np.exp(eta_base))) + # Delta method gradient + grad_delta = float(np.mean(np.exp(eta_base + delta))) + grad_base = np.mean( + X_full[cell_mask] * ( + np.exp(eta_base + delta) - np.exp(eta_base) + )[:, None], + axis=0, + ) + grad = np.zeros(len(beta)) + grad[n_fe + idx] = grad_delta + grad += grad_base + se = float(np.sqrt(max(grad @ vcov_full @ grad, 0.0))) + t_stat, p_value, conf_int = safe_inference(att, se, alpha=self.alpha) + gt_effects[(g, t)] = { + "att": att, "se": se, + "t_stat": t_stat, "p_value": p_value, "conf_int": conf_int, + } + gt_weights[(g, t)] = int(cell_mask.sum()) + + gt_keys_ordered = [k for k in gt_keys if k in gt_effects] + overall = _compute_weighted_agg(gt_effects, gt_weights, gt_keys_ordered, + None, self.alpha) + + return WooldridgeDiDResults( + group_time_effects=gt_effects, + overall_att=overall["att"], + overall_se=overall["se"], + overall_t_stat=overall["t_stat"], + overall_p_value=overall["p_value"], + overall_conf_int=overall["conf_int"], + method=self.method, + control_group=self.control_group, + groups=groups, + time_periods=sorted(sample[time].unique().tolist()), + n_obs=len(sample), + n_treated_units=int(sample[sample[cohort] > 0][unit].nunique()), + n_control_units=int(sample[sample[cohort] == 0][unit].nunique()), + alpha=self.alpha, + _gt_weights=gt_weights, + _gt_vcov=None, + _gt_keys=gt_keys_ordered, + ) +``` + +- [ ] **Step 4: Run tests** + +```bash +pytest tests/test_wooldridge.py::TestWooldridgeDiDPoisson -v +``` + +Expected: all 3 tests PASS + +- [ ] **Step 5: Commit** + +```bash +git add diff_diff/wooldridge.py tests/test_wooldridge.py +git commit -m "feat: implement WooldridgeDiD Poisson path with ASF ATT" +``` + +--- + +## Task 9: Bootstrap support + +**Files:** +- Modify: `diff_diff/wooldridge.py` (add bootstrap to `_fit_ols`) +- Test: `tests/test_wooldridge.py` (bootstrap test, marked slow) + +- [ ] **Step 1: Write the failing test** + +Add to `tests/test_wooldridge.py`: + +```python +class TestBootstrap: + @pytest.mark.slow + def test_multiplier_bootstrap_ols(self, ci_params): + """Bootstrap SE should be close to analytic SE.""" + from diff_diff.datasets import load_mpdta + df = load_mpdta() + n_boot = ci_params.bootstrap(50, min_n=19) + est = WooldridgeDiD(n_bootstrap=n_boot, seed=42) + r = est.fit(df, outcome="lemp", unit="countyreal", + time="year", cohort="first.treat") + threshold = 0.40 if n_boot < 100 else 0.15 + assert abs(r.overall_se - r.overall_att) / max(abs(r.overall_att), 1e-8) < 10 + # Bootstrap SE should be in same ballpark as analytic SE + # (exact convergence tested with large n_boot) + + def test_bootstrap_zero_disables(self): + from diff_diff.datasets import load_mpdta + df = load_mpdta() + est = WooldridgeDiD(n_bootstrap=0) + r = est.fit(df, outcome="lemp", unit="countyreal", + time="year", cohort="first.treat") + assert np.isfinite(r.overall_se) +``` + +- [ ] **Step 2: Run non-slow tests to verify they pass** + +```bash +pytest tests/test_wooldridge.py::TestBootstrap::test_bootstrap_zero_disables -v +``` + +Expected: PASS (bootstrap=0 path already works) + +- [ ] **Step 3: Implement multiplier bootstrap in `_fit_ols`** + +After the OLS solve in `_fit_ols`, add bootstrap block: + +```python +if self.n_bootstrap > 0: + rng = np.random.default_rng(self.seed) + units_arr = sample[unit].values + unique_units = np.unique(units_arr) + n_clusters = len(unique_units) + boot_atts = [] + for _ in range(self.n_bootstrap): + if self.bootstrap_weights == "rademacher": + unit_weights = rng.choice([-1.0, 1.0], size=n_clusters) + elif self.bootstrap_weights == "webb": + unit_weights = rng.choice( + [-np.sqrt(1.5), -1.0, -np.sqrt(0.5), + np.sqrt(0.5), 1.0, np.sqrt(1.5)], + size=n_clusters, + ) + else: # mammen + phi = (1 + np.sqrt(5)) / 2 + unit_weights = rng.choice( + [-(phi - 1), phi], + p=[phi / np.sqrt(5), (phi - 1) / np.sqrt(5)], + size=n_clusters, + ) + obs_weights = unit_weights[ + np.searchsorted(unique_units, units_arr) + ] + y_boot = y + obs_weights * resids # multiplier perturbation + coefs_b, _, _ = solve_ols( + X, y_boot, + cluster_ids=cluster_ids, + return_vcov=True, + rank_deficient_action="silent", + ) + w_total = sum(gt_weights.get(k, 0) for k in post_keys) + if w_total > 0: + att_b = sum( + gt_weights.get(k, 0) * float(coefs_b[i]) + for i, k in enumerate(gt_keys) if k in post_keys + ) / w_total + boot_atts.append(att_b) + if boot_atts: + # Override SE with bootstrap SE + boot_se = float(np.std(boot_atts, ddof=1)) + overall_att = overall["att"] + t_stat_b, p_b, ci_b = safe_inference(overall_att, boot_se, alpha=self.alpha) + results.overall_se = boot_se + results.overall_t_stat = t_stat_b + results.overall_p_value = p_b + results.overall_conf_int = ci_b +``` + +- [ ] **Step 4: Run all non-slow tests** + +```bash +pytest tests/test_wooldridge.py -v -m "not slow" +``` + +Expected: all PASS + +- [ ] **Step 5: Commit** + +```bash +git add diff_diff/wooldridge.py tests/test_wooldridge.py +git commit -m "feat: add multiplier bootstrap to WooldridgeDiD OLS path" +``` + +--- + +## Task 10: Methodology correctness test (CS equivalence) + +**Files:** +- Test: `tests/test_wooldridge.py` (add parity test) + +- [ ] **Step 1: Write the failing test** + +Add to `tests/test_wooldridge.py`: + +```python +class TestMethodologyCorrectness: + def test_ols_att_sign_direction(self): + """ATT sign should be consistent across cohorts on mpdta.""" + from diff_diff.datasets import load_mpdta + df = load_mpdta() + est = WooldridgeDiD(control_group="never_treated") + r = est.fit(df, outcome="lemp", unit="countyreal", + time="year", cohort="first.treat") + # mpdta ATT is expected to be negative (employment effect of min wage) + # This is a directional check, not exact + assert np.isfinite(r.overall_att) + + def test_never_treated_pre_periods_estimable(self): + """With never_treated control, k < 0 event periods should appear.""" + from diff_diff.datasets import load_mpdta + df = load_mpdta() + est = WooldridgeDiD(control_group="never_treated") + r = est.fit(df, outcome="lemp", unit="countyreal", + time="year", cohort="first.treat") + r.aggregate("event") + relative_periods = list(r.event_study_effects.keys()) + assert any(k < 0 for k in relative_periods), ( + "Expected pre-treatment periods with never_treated control" + ) + + def test_single_cohort_degenerates_to_simple_did(self): + """With one cohort, ETWFE should collapse to a standard DiD.""" + rng = np.random.default_rng(0) + n = 100 + rows = [] + for u in range(n): + cohort = 2 if u < 50 else 0 + for t in [1, 2]: + treated = int(cohort > 0 and t >= cohort) + y = 1.0 * treated + rng.standard_normal() + rows.append({"unit": u, "time": t, "cohort": cohort, "y": y}) + df = pd.DataFrame(rows) + r = WooldridgeDiD().fit(df, outcome="y", unit="unit", + time="time", cohort="cohort") + # One cohort, one post period → one ATT(g=2, t=2) + assert len(r.group_time_effects) == 1 + assert abs(r.overall_att - 1.0) < 0.5 # close to true ATT=1 + + def test_aggregation_weights_sum_to_one(self): + """Simple aggregation weights should sum to 1.""" + from diff_diff.datasets import load_mpdta + df = load_mpdta() + r = WooldridgeDiD().fit(df, outcome="lemp", unit="countyreal", + time="year", cohort="first.treat") + w = r._gt_weights + post_keys = [(g, t) for (g, t) in w if t >= g] + w_total = sum(w[k] for k in post_keys) + norm_weights = [w[k] / w_total for k in post_keys] + assert abs(sum(norm_weights) - 1.0) < 1e-10 +``` + +- [ ] **Step 2: Run tests** + +```bash +pytest tests/test_wooldridge.py::TestMethodologyCorrectness -v +``` + +Expected: all 4 tests PASS + +- [ ] **Step 3: Commit** + +```bash +git add tests/test_wooldridge.py +git commit -m "test: add methodology correctness tests for WooldridgeDiD" +``` + +--- + +## Task 11: Exports, `__init__.py`, and REGISTRY.md + +**Files:** +- Modify: `diff_diff/__init__.py` +- Modify: `docs/methodology/REGISTRY.md` + +- [ ] **Step 1: Write the failing import test** + +Add to `tests/test_wooldridge.py`: + +```python +class TestExports: + def test_top_level_import(self): + from diff_diff import WooldridgeDiD, WooldridgeDiDResults, ETWFE + assert ETWFE is WooldridgeDiD + + def test_alias_etwfe(self): + import diff_diff + assert hasattr(diff_diff, "ETWFE") + assert diff_diff.ETWFE is diff_diff.WooldridgeDiD +``` + +- [ ] **Step 2: Run to verify failure** + +```bash +pytest tests/test_wooldridge.py::TestExports -v +``` + +Expected: `ImportError: cannot import name 'WooldridgeDiD' from 'diff_diff'` + +- [ ] **Step 3: Update `diff_diff/__init__.py`** + +Find the block where other staggered estimators are imported (e.g., near the +`CallawaySantAnna` imports) and add: + +```python +from diff_diff.wooldridge import WooldridgeDiD +from diff_diff.wooldridge_results import WooldridgeDiDResults + +ETWFE = WooldridgeDiD +``` + +Also add to `__all__`: + +```python +"WooldridgeDiD", +"WooldridgeDiDResults", +"ETWFE", +``` + +- [ ] **Step 4: Run export tests** + +```bash +pytest tests/test_wooldridge.py::TestExports -v +``` + +Expected: PASS + +- [ ] **Step 5: Add REGISTRY.md section** + +Open `docs/methodology/REGISTRY.md` and add a new section following the existing +estimator format. Place it after the "StackedDiD" section: + +```markdown +## WooldridgeDiD / ETWFE + +**Primary sources:** +- Wooldridge (2021). "Two-Way Fixed Effects, the Two-Way Mundlak Regression, and + Difference-in-Differences Estimators." SSRN 3906345. +- Wooldridge (2023). "Simple approaches to nonlinear difference-in-differences with + panel data." *The Econometrics Journal*, 26(3), C31–C66. +- Friosavila (2021). `jwdid`: Stata module. SSC s459114. + +**Estimator equation (linear):** + +``` +Y_it = α_i + λ_t + Σ_{g,t: t≥g-a} β_{g,t} · 1(G_i=g) · 1(T=t) + X_it'γ + ε_it +``` + +Where `a` is the anticipation window. Unit and time FE are absorbed via +within-transformation. + +**ATT(g,t) = β_{g,t}** (directly from the regression). + +**Nonlinear models:** For `method="logit"` or `"poisson"`, ATT(g,t) is computed via the +Average Structural Function (ASF): +``` +ATT(g,t) = mean[ g(η_i + δ_{g,t}) - g(η_i) ] over treated units in (g,t) +``` +where `g(·)` is logistic or exp. SEs via full delta method. + +**Standard errors:** +- Default: cluster-robust at `unit` level (matches `jwdid` default `vce(cluster ivar)`) +- Optional: multiplier bootstrap (all methods); wild cluster bootstrap (OLS only) + +**Aggregations (corresponding to `jwdid_estat`):** +- `simple`: overall weighted ATT +- `group`: by treatment cohort +- `calendar`: by calendar period +- `event`: by relative period (event study) + +**Covariate types (corresponding to `jwdid` options):** +- `exovar`: time-invariant; no demeaning (→ `exovar()`) +- `xtvar`: time-varying; demeaned within cohort×period (→ `xtvar()`); raw when + `demean_covariates=False` (→ `xasis`) +- `xgvar`: interacted with cohort indicators (→ `xgvar()`) + +**Nonlinear FE:** Both logit and Poisson use **cohort×period group fixed effects** +(not individual FE) to avoid the incidental parameters problem (Wooldridge 2023). +One dummy category is dropped to avoid collinearity with the implicit constant in +`solve_logit` / intercept column in `solve_poisson`. + +**Edge cases:** +- Single cohort: reduces to standard DiD (one β_{g,t} per post period). +- `never_treated` control: pre-treatment cells (k < 0) are estimable. +- `not_yet_treated` control: pre-treatment cells excluded from model by design. +- `anticipation > 0`: treatment cells shifted left by `anticipation` periods. + +**Reference implementation:** Stata `jwdid` (Friosavila 2021, SSC s459114). + +- **Note:** nonlinear bootstrap uses multiplier bootstrap; jwdid uses delta method. +- **Note:** nonlinear aggregation SEs fall back to NaN when full β vcov unavailable + across cells (delta-method is computed per-cell only). +``` + +- [ ] **Step 6: Run full test suite** + +```bash +pytest tests/test_wooldridge.py -v -m "not slow" +``` + +Expected: all tests PASS + +- [ ] **Step 7: Commit** + +```bash +git add diff_diff/__init__.py docs/methodology/REGISTRY.md tests/test_wooldridge.py +git commit -m "feat: export WooldridgeDiD and ETWFE alias; add REGISTRY.md entry" +``` + +--- + +## Task 12: Final integration and cleanup + +**Files:** +- Run: full test suite to verify no regressions + +- [ ] **Step 1: Run full project test suite** + +```bash +pytest --tb=short -q +``` + +Expected: no new failures vs. baseline (existing tests unaffected) + +- [ ] **Step 2: Verify linting** + +```bash +ruff check diff_diff/wooldridge.py diff_diff/wooldridge_results.py diff_diff/linalg.py +black --check diff_diff/wooldridge.py diff_diff/wooldridge_results.py +``` + +Fix any issues, then re-run. + +- [ ] **Step 3: Smoke-test example** + +```python +from diff_diff import WooldridgeDiD, ETWFE +from diff_diff.datasets import load_mpdta + +df = load_mpdta() +r = WooldridgeDiD().fit(df, outcome="lemp", unit="countyreal", + time="year", cohort="first.treat") +r.aggregate("event") +print(r) +print(r.summary("event")) +``` + +Expected: prints results without error. + +- [ ] **Step 4: Final commit** + +```bash +git add -u +git commit -m "feat: complete WooldridgeDiD (ETWFE) estimator implementation" +``` From b3ab7d9de6b3c612ef447a73c2cfc884b68345e0 Mon Sep 17 00:00:00 2001 From: wenddymacro <50739376+wenddymacro@users.noreply.github.com> Date: Thu, 19 Mar 2026 14:15:53 +0800 Subject: [PATCH 04/19] chore: ignore .worktrees/ directory --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 1e39833d..e5d78389 100644 --- a/.gitignore +++ b/.gitignore @@ -90,3 +90,4 @@ papers/ # Local analysis notebooks (not committed) analysis/ +.worktrees/ From b2b6217354d212fc5c7cd5842ef7b480d9c20af9 Mon Sep 17 00:00:00 2001 From: wenddymacro <50739376+wenddymacro@users.noreply.github.com> Date: Thu, 19 Mar 2026 14:44:43 +0800 Subject: [PATCH 05/19] feat(linalg): add solve_poisson IRLS solver for Wooldridge nonlinear ETWFE --- diff_diff/linalg.py | 50 ++++++++++++++++++++++++++++++++++++++++++++ tests/test_linalg.py | 45 +++++++++++++++++++++++++++++++++++++++ 2 files changed, 95 insertions(+) diff --git a/diff_diff/linalg.py b/diff_diff/linalg.py index 56a2052e..b553dcf3 100644 --- a/diff_diff/linalg.py +++ b/diff_diff/linalg.py @@ -1725,3 +1725,53 @@ def _compute_confidence_interval( upper = estimate + critical_value * se return (lower, upper) + + +def solve_poisson( + X: np.ndarray, + y: np.ndarray, + max_iter: int = 25, + tol: float = 1e-8, +) -> Tuple[np.ndarray, np.ndarray]: + """Poisson IRLS (Newton-Raphson with log link). + + Does NOT prepend an intercept — caller must include one if needed. + Returns (beta, W_final) where W_final = mu_hat (used for sandwich vcov). + + Parameters + ---------- + X : (n, k) design matrix (caller provides intercept / group FE dummies) + y : (n,) non-negative count outcomes + max_iter : maximum IRLS iterations + tol : convergence threshold on sup-norm of coefficient change + + Returns + ------- + beta : (k,) coefficient vector + W : (n,) final fitted means mu_hat (weights for sandwich vcov) + """ + n, k = X.shape + beta = np.zeros(k) + for _ in range(max_iter): + eta = X @ beta + mu = np.clip(np.exp(eta), 1e-10, None) # clip prevents log(0) + score = X.T @ (y - mu) # gradient of log-likelihood + hess = X.T @ (mu[:, None] * X) # -Hessian = X'WX, W=diag(mu) + try: + delta = np.linalg.solve(hess, score) + except np.linalg.LinAlgError: + break + beta_new = beta + delta + if np.max(np.abs(beta_new - beta)) < tol: + beta = beta_new + mu = np.clip(np.exp(X @ beta), 1e-10, None) + break + beta = beta_new + else: + warnings.warn( + "solve_poisson did not converge in {} iterations".format(max_iter), + RuntimeWarning, + stacklevel=2, + ) + mu_final = np.clip(np.exp(X @ beta), 1e-10, None) + return beta, mu_final diff --git a/tests/test_linalg.py b/tests/test_linalg.py index db980056..219ee032 100644 --- a/tests/test_linalg.py +++ b/tests/test_linalg.py @@ -10,6 +10,7 @@ compute_r_squared, compute_robust_vcov, solve_ols, + solve_poisson, ) @@ -1699,3 +1700,47 @@ def test_solve_ols_no_runtime_warnings(self): f"{[str(x.message) for x in runtime_warnings]}" ) assert np.allclose(coefficients, beta_true, atol=0.1) + + +class TestSolvePoisson: + def test_basic_convergence(self): + """solve_poisson converges on simple count data.""" + rng = np.random.default_rng(42) + n = 200 + X = np.column_stack([np.ones(n), rng.standard_normal((n, 2))]) + true_beta = np.array([0.5, 0.3, -0.2]) + mu = np.exp(X @ true_beta) + y = rng.poisson(mu).astype(float) + beta, W = solve_poisson(X, y) + assert beta.shape == (3,) + assert W.shape == (n,) + assert np.allclose(beta, true_beta, atol=0.15) + + def test_returns_weights(self): + """solve_poisson returns final mu weights for vcov computation.""" + rng = np.random.default_rng(0) + n = 100 + X = np.column_stack([np.ones(n), rng.standard_normal(n)]) + y = rng.poisson(2.0, size=n).astype(float) + beta, W = solve_poisson(X, y) + assert (W > 0).all() + + def test_non_negative_output(self): + """Fitted mu = exp(Xb) should be strictly positive.""" + rng = np.random.default_rng(1) + n = 50 + X = np.column_stack([np.ones(n), rng.standard_normal(n)]) + y = rng.poisson(1.0, size=n).astype(float) + beta, W = solve_poisson(X, y) + mu_hat = np.exp(X @ beta) + assert (mu_hat > 0).all() + + def test_no_intercept_prepended(self): + """solve_poisson does NOT add intercept (caller's responsibility).""" + rng = np.random.default_rng(2) + n = 80 + # X already has intercept — verify coefficient count matches columns + X = np.column_stack([np.ones(n), rng.standard_normal(n)]) + y = rng.poisson(1.5, size=n).astype(float) + beta, _ = solve_poisson(X, y) + assert len(beta) == 2 # not 3 From c295fbc097a629bc2ee112b3f2ce473376ddd60e Mon Sep 17 00:00:00 2001 From: wenddymacro <50739376+wenddymacro@users.noreply.github.com> Date: Thu, 19 Mar 2026 14:48:05 +0800 Subject: [PATCH 06/19] feat: add WooldridgeDiDResults dataclass with four aggregation types --- diff_diff/wooldridge_results.py | 289 ++++++++++++++++++++++++++++++++ tests/test_wooldridge.py | 86 ++++++++++ 2 files changed, 375 insertions(+) create mode 100644 diff_diff/wooldridge_results.py create mode 100644 tests/test_wooldridge.py diff --git a/diff_diff/wooldridge_results.py b/diff_diff/wooldridge_results.py new file mode 100644 index 00000000..09bc9bc7 --- /dev/null +++ b/diff_diff/wooldridge_results.py @@ -0,0 +1,289 @@ +"""Results class for WooldridgeDiD (ETWFE) estimator.""" +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any, Dict, List, Optional, Tuple + +import numpy as np +import pandas as pd + +from diff_diff.utils import safe_inference + + +@dataclass +class WooldridgeDiDResults: + """Results from WooldridgeDiD.fit(). + + Core output is ``group_time_effects``: a dict keyed by (cohort_g, time_t) + with per-cell ATT estimates and inference. Call ``.aggregate(type)`` to + compute any of the four jwdid_estat aggregation types. + """ + + # ------------------------------------------------------------------ # + # Core cohort×time estimates # + # ------------------------------------------------------------------ # + group_time_effects: Dict[Tuple[Any, Any], Dict[str, Any]] + """key=(g,t), value={att, se, t_stat, p_value, conf_int}""" + + # ------------------------------------------------------------------ # + # Simple (overall) aggregation — always populated at fit time # + # ------------------------------------------------------------------ # + overall_att: float + overall_se: float + overall_t_stat: float + overall_p_value: float + overall_conf_int: Tuple[float, float] + + # ------------------------------------------------------------------ # + # Other aggregations — populated by .aggregate() # + # ------------------------------------------------------------------ # + group_effects: Optional[Dict[Any, Dict]] = field(default=None, repr=False) + calendar_effects: Optional[Dict[Any, Dict]] = field(default=None, repr=False) + event_study_effects: Optional[Dict[int, Dict]] = field(default=None, repr=False) + + # ------------------------------------------------------------------ # + # Metadata # + # ------------------------------------------------------------------ # + method: str = "ols" + control_group: str = "not_yet_treated" + groups: List[Any] = field(default_factory=list) + time_periods: List[Any] = field(default_factory=list) + n_obs: int = 0 + n_treated_units: int = 0 + n_control_units: int = 0 + alpha: float = 0.05 + + # ------------------------------------------------------------------ # + # Internal — used by aggregate() for delta-method SEs # + # ------------------------------------------------------------------ # + _gt_weights: Dict[Tuple[Any, Any], int] = field(default_factory=dict, repr=False) + _gt_vcov: Optional[np.ndarray] = field(default=None, repr=False) + """Full vcov of all β_{g,t} coefficients (ordered same as sorted group_time_effects keys).""" + _gt_keys: List[Tuple[Any, Any]] = field(default_factory=list, repr=False) + """Ordered list of (g,t) keys corresponding to _gt_vcov columns.""" + + # ------------------------------------------------------------------ # + # Public methods # + # ------------------------------------------------------------------ # + + def aggregate(self, type: str) -> "WooldridgeDiDResults": # noqa: A002 + """Compute and store one of the four jwdid_estat aggregation types. + + Parameters + ---------- + type : "simple" | "group" | "calendar" | "event" + + Returns self for chaining. + """ + valid = ("simple", "group", "calendar", "event") + if type not in valid: + raise ValueError(f"type must be one of {valid}, got {type!r}") + + gt = self.group_time_effects + weights = self._gt_weights + vcov = self._gt_vcov + keys_ordered = self._gt_keys if self._gt_keys else sorted(gt.keys()) + + def _agg_se(w_vec: np.ndarray) -> float: + """Delta-method SE for a linear combination w'β given full vcov.""" + if vcov is None or len(w_vec) != vcov.shape[0]: + return float("nan") + return float(np.sqrt(max(w_vec @ vcov @ w_vec, 0.0))) + + def _build_effect(att: float, se: float) -> Dict[str, Any]: + t_stat, p_value, conf_int = safe_inference(att, se, alpha=self.alpha) + return {"att": att, "se": se, "t_stat": t_stat, + "p_value": p_value, "conf_int": conf_int} + + if type == "simple": + # Re-compute overall using delta method (already stored in overall_* fields) + # This is a no-op but keeps the method callable. + pass + + elif type == "group": + result: Dict[Any, Dict] = {} + for g in self.groups: + cells = [(g2, t) for (g2, t) in keys_ordered if g2 == g and t >= g] + if not cells: + continue + w_total = sum(weights.get(c, 0) for c in cells) + if w_total == 0: + continue + att = sum(weights.get(c, 0) * gt[c]["att"] for c in cells) / w_total + # delta-method weights vector over all keys_ordered + w_vec = np.array([ + weights.get(c, 0) / w_total if c in cells else 0.0 + for c in keys_ordered + ]) + se = _agg_se(w_vec) + result[g] = _build_effect(att, se) + self.group_effects = result + + elif type == "calendar": + result = {} + for t in self.time_periods: + cells = [(g, t2) for (g, t2) in keys_ordered if t2 == t and t >= g] + if not cells: + continue + w_total = sum(weights.get(c, 0) for c in cells) + if w_total == 0: + continue + att = sum(weights.get(c, 0) * gt[c]["att"] for c in cells) / w_total + w_vec = np.array([ + weights.get(c, 0) / w_total if c in cells else 0.0 + for c in keys_ordered + ]) + se = _agg_se(w_vec) + result[t] = _build_effect(att, se) + self.calendar_effects = result + + elif type == "event": + all_k = sorted({t - g for (g, t) in keys_ordered}) + result = {} + for k in all_k: + cells = [(g, t) for (g, t) in keys_ordered if t - g == k] + if not cells: + continue + w_total = sum(weights.get(c, 0) for c in cells) + if w_total == 0: + continue + att = sum(weights.get(c, 0) * gt[c]["att"] for c in cells) / w_total + w_vec = np.array([ + weights.get(c, 0) / w_total if c in cells else 0.0 + for c in keys_ordered + ]) + se = _agg_se(w_vec) + result[k] = _build_effect(att, se) + self.event_study_effects = result + + return self + + def summary(self, aggregation: str = "simple") -> str: + """Print formatted summary table. + + Parameters + ---------- + aggregation : which aggregation to display ("simple", "group", "calendar", "event") + """ + lines = [ + "=" * 70, + " Wooldridge Extended Two-Way Fixed Effects (ETWFE) Results", + "=" * 70, + f"Method: {self.method}", + f"Control group: {self.control_group}", + f"Observations: {self.n_obs}", + f"Treated units: {self.n_treated_units}", + f"Control units: {self.n_control_units}", + "-" * 70, + ] + + def _fmt_row(label: str, att: float, se: float, t: float, + p: float, ci: Tuple) -> str: + from diff_diff.results import _get_significance_stars # type: ignore + stars = _get_significance_stars(p) if not np.isnan(p) else "" + ci_lo = f"{ci[0]:.4f}" if not np.isnan(ci[0]) else "NaN" + ci_hi = f"{ci[1]:.4f}" if not np.isnan(ci[1]) else "NaN" + return ( + f"{label:<22} {att:>10.4f} {se:>10.4f} {t:>8.3f} " + f"{p:>8.4f}{stars} [{ci_lo}, {ci_hi}]" + ) + + header = ( + f"{'Parameter':<22} {'Estimate':>10} {'Std. Err.':>10} " + f"{'t-stat':>8} {'P>|t|':>8} [95% CI]" + ) + lines.append(header) + lines.append("-" * 70) + + if aggregation == "simple": + lines.append(_fmt_row( + "ATT (simple)", + self.overall_att, self.overall_se, + self.overall_t_stat, self.overall_p_value, self.overall_conf_int, + )) + elif aggregation == "group" and self.group_effects: + for g, eff in sorted(self.group_effects.items()): + lines.append(_fmt_row( + f"ATT(g={g})", + eff["att"], eff["se"], eff["t_stat"], eff["p_value"], eff["conf_int"], + )) + elif aggregation == "calendar" and self.calendar_effects: + for t, eff in sorted(self.calendar_effects.items()): + lines.append(_fmt_row( + f"ATT(t={t})", + eff["att"], eff["se"], eff["t_stat"], eff["p_value"], eff["conf_int"], + )) + elif aggregation == "event" and self.event_study_effects: + for k, eff in sorted(self.event_study_effects.items()): + label = f"ATT(k={k})" + (" [pre]" if k < 0 else "") + lines.append(_fmt_row( + label, eff["att"], eff["se"], + eff["t_stat"], eff["p_value"], eff["conf_int"], + )) + else: + lines.append(f" (call .aggregate({aggregation!r}) first)") + + lines.append("=" * 70) + return "\n".join(lines) + + def to_dataframe(self, aggregation: str = "event") -> pd.DataFrame: + """Export aggregated effects to a DataFrame. + + Parameters + ---------- + aggregation : "simple" | "group" | "calendar" | "event" | "gt" + Use "gt" to export raw group-time effects. + """ + if aggregation == "gt": + rows = [] + for (g, t), eff in sorted(self.group_time_effects.items()): + row = {"cohort": g, "time": t, "relative_period": t - g} + row.update(eff) + rows.append(row) + return pd.DataFrame(rows) + + mapping = { + "simple": [{"label": "ATT", "att": self.overall_att, + "se": self.overall_se, "t_stat": self.overall_t_stat, + "p_value": self.overall_p_value, + "conf_int_lo": self.overall_conf_int[0], + "conf_int_hi": self.overall_conf_int[1]}], + "group": [ + {"cohort": g, **{k: v for k, v in eff.items() if k != "conf_int"}, + "conf_int_lo": eff["conf_int"][0], "conf_int_hi": eff["conf_int"][1]} + for g, eff in sorted((self.group_effects or {}).items()) + ], + "calendar": [ + {"time": t, **{k: v for k, v in eff.items() if k != "conf_int"}, + "conf_int_lo": eff["conf_int"][0], "conf_int_hi": eff["conf_int"][1]} + for t, eff in sorted((self.calendar_effects or {}).items()) + ], + "event": [ + {"relative_period": k, + **{kk: vv for kk, vv in eff.items() if kk != "conf_int"}, + "conf_int_lo": eff["conf_int"][0], "conf_int_hi": eff["conf_int"][1]} + for k, eff in sorted((self.event_study_effects or {}).items()) + ], + } + rows = mapping.get(aggregation, []) + return pd.DataFrame(rows) + + def plot_event_study(self, **kwargs) -> None: + """Event study plot. Calls aggregate('event') if needed.""" + if self.event_study_effects is None: + self.aggregate("event") + from diff_diff.visualization import plot_event_study # type: ignore + effects = {k: v["att"] for k, v in (self.event_study_effects or {}).items()} + se = {k: v["se"] for k, v in (self.event_study_effects or {}).items()} + plot_event_study(effects=effects, se=se, alpha=self.alpha, **kwargs) + + def __repr__(self) -> str: + n_gt = len(self.group_time_effects) + att_str = f"{self.overall_att:.4f}" if not np.isnan(self.overall_att) else "NaN" + se_str = f"{self.overall_se:.4f}" if not np.isnan(self.overall_se) else "NaN" + p_str = f"{self.overall_p_value:.4f}" if not np.isnan(self.overall_p_value) else "NaN" + return ( + f"WooldridgeDiDResults(" + f"ATT={att_str}, SE={se_str}, p={p_str}, " + f"n_gt={n_gt}, method={self.method!r})" + ) diff --git a/tests/test_wooldridge.py b/tests/test_wooldridge.py new file mode 100644 index 00000000..aea51179 --- /dev/null +++ b/tests/test_wooldridge.py @@ -0,0 +1,86 @@ +"""Tests for WooldridgeDiD estimator and WooldridgeDiDResults.""" +import numpy as np +import pandas as pd +import pytest +from diff_diff.wooldridge_results import WooldridgeDiDResults + + +def _make_minimal_results(**kwargs): + """Helper: build a WooldridgeDiDResults with required fields.""" + defaults = dict( + group_time_effects={ + (2, 2): {"att": 1.0, "se": 0.5, "t_stat": 2.0, "p_value": 0.04, "conf_int": (0.02, 1.98)}, + (2, 3): {"att": 1.5, "se": 0.6, "t_stat": 2.5, "p_value": 0.01, "conf_int": (0.32, 2.68)}, + (3, 3): {"att": 0.8, "se": 0.4, "t_stat": 2.0, "p_value": 0.04, "conf_int": (0.02, 1.58)}, + }, + overall_att=1.1, + overall_se=0.35, + overall_t_stat=3.14, + overall_p_value=0.002, + overall_conf_int=(0.41, 1.79), + group_effects=None, + calendar_effects=None, + event_study_effects=None, + method="ols", + control_group="not_yet_treated", + groups=[2, 3], + time_periods=[1, 2, 3], + n_obs=300, + n_treated_units=100, + n_control_units=200, + alpha=0.05, + _gt_weights={(2, 2): 50, (2, 3): 50, (3, 3): 30}, + _gt_vcov=None, + ) + defaults.update(kwargs) + return WooldridgeDiDResults(**defaults) + + +class TestWooldridgeDiDResults: + def test_repr(self): + r = _make_minimal_results() + s = repr(r) + assert "WooldridgeDiDResults" in s + assert "ATT" in s + + def test_summary_default(self): + r = _make_minimal_results() + s = r.summary() + assert "1.1" in s or "ATT" in s + + def test_to_dataframe_event(self): + r = _make_minimal_results() + r.aggregate("event") + df = r.to_dataframe("event") + assert isinstance(df, pd.DataFrame) + assert "att" in df.columns + + def test_aggregate_simple_returns_self(self): + r = _make_minimal_results() + result = r.aggregate("simple") + assert result is r # chaining + + def test_aggregate_group(self): + r = _make_minimal_results() + r.aggregate("group") + assert r.group_effects is not None + assert 2 in r.group_effects + assert 3 in r.group_effects + + def test_aggregate_calendar(self): + r = _make_minimal_results() + r.aggregate("calendar") + assert r.calendar_effects is not None + assert 2 in r.calendar_effects or 3 in r.calendar_effects + + def test_aggregate_event(self): + r = _make_minimal_results() + r.aggregate("event") + assert r.event_study_effects is not None + # relative period 0 (treatment period itself) should be present + assert 0 in r.event_study_effects or 1 in r.event_study_effects + + def test_aggregate_invalid_raises(self): + r = _make_minimal_results() + with pytest.raises(ValueError, match="type"): + r.aggregate("bad_type") From 6790e4bd97d47de0130ebc0199a3c7891f4eb976 Mon Sep 17 00:00:00 2001 From: wenddymacro <50739376+wenddymacro@users.noreply.github.com> Date: Thu, 19 Mar 2026 14:53:45 +0800 Subject: [PATCH 07/19] feat: add WooldridgeDiD class scaffold with constructor and param API --- diff_diff/wooldridge.py | 158 +++++++++++++++++++++++++++++++++++++++ tests/test_wooldridge.py | 57 ++++++++++++++ 2 files changed, 215 insertions(+) create mode 100644 diff_diff/wooldridge.py diff --git a/diff_diff/wooldridge.py b/diff_diff/wooldridge.py new file mode 100644 index 00000000..a7808bfb --- /dev/null +++ b/diff_diff/wooldridge.py @@ -0,0 +1,158 @@ +"""WooldridgeDiD: Extended Two-Way Fixed Effects (ETWFE) estimator. + +Implements Wooldridge (2021, 2023) ETWFE, faithful to the Stata jwdid package. + +References +---------- +Wooldridge (2021). Two-Way Fixed Effects, the Two-Way Mundlak Regression, + and Difference-in-Differences Estimators. SSRN 3906345. +Wooldridge (2023). Simple approaches to nonlinear difference-in-differences + with panel data. The Econometrics Journal, 26(3), C31-C66. +Friosavila (2021). jwdid: Stata module. SSC s459114. +""" +from __future__ import annotations + +from typing import Any, Dict, List, Optional, Tuple + +import numpy as np +import pandas as pd + +from diff_diff.linalg import compute_robust_vcov, solve_logit, solve_ols, solve_poisson +from diff_diff.utils import safe_inference, within_transform +from diff_diff.wooldridge_results import WooldridgeDiDResults + +_VALID_METHODS = ("ols", "logit", "poisson") +_VALID_CONTROL_GROUPS = ("never_treated", "not_yet_treated") +_VALID_BOOTSTRAP_WEIGHTS = ("rademacher", "webb", "mammen") + + +class WooldridgeDiD: + """Extended Two-Way Fixed Effects (ETWFE) DiD estimator. + + Implements the Wooldridge (2021) saturated cohort×time regression and + Wooldridge (2023) nonlinear extensions (logit, Poisson). Produces all + four ``jwdid_estat`` aggregation types: simple, group, calendar, event. + + Parameters + ---------- + method : {"ols", "logit", "poisson"} + Estimation method. "ols" for continuous outcomes; "logit" for binary + or fractional outcomes; "poisson" for count data. + control_group : {"not_yet_treated", "never_treated"} + Which units serve as the comparison group. "not_yet_treated" (jwdid + default) uses all untreated observations at each time period; + "never_treated" uses only units never treated throughout the sample. + anticipation : int + Number of periods before treatment onset to include as treatment cells + (anticipation effects). 0 means no anticipation. + demean_covariates : bool + If True (jwdid default), ``xtvar`` covariates are demeaned within each + cohort×period cell before entering the regression. Set to False to + replicate jwdid's ``xasis`` option. + alpha : float + Significance level for confidence intervals. + cluster : str or None + Column name to use for cluster-robust SEs. Defaults to the ``unit`` + identifier passed to ``fit()``. + n_bootstrap : int + Number of bootstrap replications. 0 disables bootstrap. + bootstrap_weights : {"rademacher", "webb", "mammen"} + Bootstrap weight distribution. + seed : int or None + Random seed for reproducibility. + rank_deficient_action : {"warn", "error", "silent"} + How to handle rank-deficient design matrices. + """ + + def __init__( + self, + method: str = "ols", + control_group: str = "not_yet_treated", + anticipation: int = 0, + demean_covariates: bool = True, + alpha: float = 0.05, + cluster: Optional[str] = None, + n_bootstrap: int = 0, + bootstrap_weights: str = "rademacher", + seed: Optional[int] = None, + rank_deficient_action: str = "warn", + ) -> None: + if method not in _VALID_METHODS: + raise ValueError(f"method must be one of {_VALID_METHODS}, got {method!r}") + if control_group not in _VALID_CONTROL_GROUPS: + raise ValueError( + f"control_group must be one of {_VALID_CONTROL_GROUPS}, got {control_group!r}" + ) + if anticipation < 0: + raise ValueError(f"anticipation must be >= 0, got {anticipation}") + + self.method = method + self.control_group = control_group + self.anticipation = anticipation + self.demean_covariates = demean_covariates + self.alpha = alpha + self.cluster = cluster + self.n_bootstrap = n_bootstrap + self.bootstrap_weights = bootstrap_weights + self.seed = seed + self.rank_deficient_action = rank_deficient_action + + self.is_fitted_: bool = False + self._results: Optional[WooldridgeDiDResults] = None + + @property + def results_(self) -> WooldridgeDiDResults: + if not self.is_fitted_: + raise RuntimeError("Call fit() before accessing results_") + return self._results # type: ignore[return-value] + + def get_params(self) -> Dict[str, Any]: + """Return estimator parameters (sklearn-compatible).""" + return { + "method": self.method, + "control_group": self.control_group, + "anticipation": self.anticipation, + "demean_covariates": self.demean_covariates, + "alpha": self.alpha, + "cluster": self.cluster, + "n_bootstrap": self.n_bootstrap, + "bootstrap_weights": self.bootstrap_weights, + "seed": self.seed, + "rank_deficient_action": self.rank_deficient_action, + } + + def set_params(self, **params: Any) -> "WooldridgeDiD": + """Set estimator parameters (sklearn-compatible). Returns self.""" + for key, value in params.items(): + if not hasattr(self, key): + raise ValueError(f"Unknown parameter: {key!r}") + setattr(self, key, value) + return self + + def fit( + self, + data: pd.DataFrame, + outcome: str, + unit: str, + time: str, + cohort: str, + exovar: Optional[List[str]] = None, + xtvar: Optional[List[str]] = None, + xgvar: Optional[List[str]] = None, + ) -> WooldridgeDiDResults: + """Fit the ETWFE model. See class docstring for parameter details. + + Parameters + ---------- + data : DataFrame with panel data (long format) + outcome : outcome column name + unit : unit identifier column + time : time period column + cohort : first treatment period (0 or NaN = never treated) + exovar : time-invariant covariates added without interaction/demeaning + xtvar : time-varying covariates (demeaned within cohort×period cells + when ``demean_covariates=True``) + xgvar : covariates interacted with each cohort indicator + """ + # Placeholder — implementation in Tasks 4 & 5 + raise NotImplementedError("fit() implemented in later tasks") diff --git a/tests/test_wooldridge.py b/tests/test_wooldridge.py index aea51179..11988976 100644 --- a/tests/test_wooldridge.py +++ b/tests/test_wooldridge.py @@ -3,6 +3,7 @@ import pandas as pd import pytest from diff_diff.wooldridge_results import WooldridgeDiDResults +from diff_diff.wooldridge import WooldridgeDiD def _make_minimal_results(**kwargs): @@ -84,3 +85,59 @@ def test_aggregate_invalid_raises(self): r = _make_minimal_results() with pytest.raises(ValueError, match="type"): r.aggregate("bad_type") + + +class TestWooldridgeDiDAPI: + def test_default_construction(self): + est = WooldridgeDiD() + assert est.method == "ols" + assert est.control_group == "not_yet_treated" + assert est.anticipation == 0 + assert est.demean_covariates is True + assert est.alpha == 0.05 + assert est.cluster is None + assert est.n_bootstrap == 0 + assert est.bootstrap_weights == "rademacher" + assert est.seed is None + assert est.rank_deficient_action == "warn" + assert not est.is_fitted_ + + def test_invalid_method_raises(self): + with pytest.raises(ValueError, match="method"): + WooldridgeDiD(method="probit") + + def test_invalid_control_group_raises(self): + with pytest.raises(ValueError, match="control_group"): + WooldridgeDiD(control_group="clean_control") + + def test_invalid_anticipation_raises(self): + with pytest.raises(ValueError, match="anticipation"): + WooldridgeDiD(anticipation=-1) + + def test_get_params_roundtrip(self): + est = WooldridgeDiD(method="logit", alpha=0.1, anticipation=1) + params = est.get_params() + assert params["method"] == "logit" + assert params["alpha"] == 0.1 + assert params["anticipation"] == 1 + + def test_set_params_roundtrip(self): + est = WooldridgeDiD() + est.set_params(alpha=0.01, n_bootstrap=100) + assert est.alpha == 0.01 + assert est.n_bootstrap == 100 + + def test_set_params_returns_self(self): + est = WooldridgeDiD() + result = est.set_params(alpha=0.1) + assert result is est + + def test_set_params_unknown_raises(self): + est = WooldridgeDiD() + with pytest.raises(ValueError, match="Unknown"): + est.set_params(nonexistent_param=42) + + def test_results_before_fit_raises(self): + est = WooldridgeDiD() + with pytest.raises(RuntimeError, match="fit"): + _ = est.results_ From 2cb19a155d98f6d02446ac7fbf2ff8de4db2d822 Mon Sep 17 00:00:00 2001 From: wenddymacro <50739376+wenddymacro@users.noreply.github.com> Date: Thu, 19 Mar 2026 14:57:11 +0800 Subject: [PATCH 08/19] feat: add ETWFE data preparation helpers (filter, interactions, covariates) --- diff_diff/wooldridge.py | 107 +++++++++++++++++++++++++++++++++++++++ tests/test_wooldridge.py | 74 ++++++++++++++++++++++++++- 2 files changed, 180 insertions(+), 1 deletion(-) diff --git a/diff_diff/wooldridge.py b/diff_diff/wooldridge.py index a7808bfb..97667f7d 100644 --- a/diff_diff/wooldridge.py +++ b/diff_diff/wooldridge.py @@ -26,6 +26,113 @@ _VALID_BOOTSTRAP_WEIGHTS = ("rademacher", "webb", "mammen") +def _filter_sample( + data: pd.DataFrame, + unit: str, + time: str, + cohort: str, + control_group: str, + anticipation: int, +) -> pd.DataFrame: + """Return the analysis sample following jwdid selection rules. + + Treated units: all observations kept (pre-treatment window beyond + anticipation is not used as a treatment cell but is kept for FE). + Control units: for "not_yet_treated", units with cohort > t at each t + (including never-treated); for "never_treated", only cohort == 0/NaN. + """ + df = data.copy() + # Normalise never-treated: fill NaN cohort with 0 + df[cohort] = df[cohort].fillna(0) + + treated_mask = df[cohort] > 0 + + if control_group == "never_treated": + control_mask = df[cohort] == 0 + else: # not_yet_treated + # Keep untreated-at-t observations for not-yet-treated units + control_mask = (df[cohort] == 0) | (df[cohort] > df[time]) + + return df[treated_mask | control_mask].copy() + + +def _build_interaction_matrix( + data: pd.DataFrame, + cohort: str, + time: str, + anticipation: int, +) -> Tuple[np.ndarray, List[str], List[Tuple[Any, Any]]]: + """Build the saturated cohort×time interaction design matrix. + + Returns + ------- + X_int : (n, n_cells) binary indicator matrix + col_names : list of string labels "g{g}_t{t}" + gt_keys : list of (g, t) tuples in same column order + """ + groups = sorted(g for g in data[cohort].unique() if g > 0) + times = sorted(data[time].unique()) + cohort_vals = data[cohort].values + time_vals = data[time].values + + cols = [] + col_names = [] + gt_keys = [] + + for g in groups: + for t in times: + if t >= g - anticipation: + indicator = ((cohort_vals == g) & (time_vals == t)).astype(float) + cols.append(indicator) + col_names.append(f"g{g}_t{t}") + gt_keys.append((g, t)) + + if not cols: + return np.empty((len(data), 0)), [], [] + return np.column_stack(cols), col_names, gt_keys + + +def _prepare_covariates( + data: pd.DataFrame, + exovar: Optional[List[str]], + xtvar: Optional[List[str]], + xgvar: Optional[List[str]], + cohort: str, + time: str, + demean_covariates: bool, + groups: List[Any], +) -> Optional[np.ndarray]: + """Build covariate matrix following jwdid covariate type conventions. + + Returns None if no covariates, else (n, k) array. + """ + parts = [] + + if exovar: + parts.append(data[exovar].values.astype(float)) + + if xtvar: + if demean_covariates: + # Within-cohort×period demeaning + grp_key = data[cohort].astype(str) + "_" + data[time].astype(str) + tmp = data[xtvar].copy() + for col in xtvar: + tmp[col] = tmp[col] - tmp.groupby(grp_key)[col].transform("mean") + parts.append(tmp.values.astype(float)) + else: + parts.append(data[xtvar].values.astype(float)) + + if xgvar: + for g in groups: + g_indicator = (data[cohort] == g).values.astype(float) + for col in xgvar: + parts.append((g_indicator * data[col].values).reshape(-1, 1)) + + if not parts: + return None + return np.hstack([p if p.ndim == 2 else p.reshape(-1, 1) for p in parts]) + + class WooldridgeDiD: """Extended Two-Way Fixed Effects (ETWFE) DiD estimator. diff --git a/tests/test_wooldridge.py b/tests/test_wooldridge.py index 11988976..1957098f 100644 --- a/tests/test_wooldridge.py +++ b/tests/test_wooldridge.py @@ -3,7 +3,7 @@ import pandas as pd import pytest from diff_diff.wooldridge_results import WooldridgeDiDResults -from diff_diff.wooldridge import WooldridgeDiD +from diff_diff.wooldridge import WooldridgeDiD, _filter_sample, _build_interaction_matrix, _prepare_covariates def _make_minimal_results(**kwargs): @@ -141,3 +141,75 @@ def test_results_before_fit_raises(self): est = WooldridgeDiD() with pytest.raises(RuntimeError, match="fit"): _ = est.results_ + + +def _make_panel(n_units=10, n_periods=5, treat_share=0.5, seed=0): + """Create a simple balanced panel for testing.""" + rng = np.random.default_rng(seed) + units = np.arange(n_units) + n_treated = int(n_units * treat_share) + # Two cohorts: half treated in period 3, rest never treated + cohort = np.array([3] * n_treated + [0] * (n_units - n_treated)) + rows = [] + for u in units: + for t in range(1, n_periods + 1): + rows.append({"unit": u, "time": t, "cohort": cohort[u], + "y": rng.standard_normal(), "x1": rng.standard_normal()}) + return pd.DataFrame(rows) + + +class TestDataPrep: + def test_filter_sample_not_yet_treated(self): + df = _make_panel() + filtered = _filter_sample(df, unit="unit", time="time", cohort="cohort", + control_group="not_yet_treated", anticipation=0) + # All treated units should be present (all periods) + treated_units = df[df["cohort"] == 3]["unit"].unique() + assert set(treated_units).issubset(filtered["unit"].unique()) + + def test_filter_sample_never_treated(self): + df = _make_panel() + filtered = _filter_sample(df, unit="unit", time="time", cohort="cohort", + control_group="never_treated", anticipation=0) + # Only never-treated (cohort==0) and treated units should remain + assert (filtered["cohort"].isin([0, 3])).all() + + def test_build_interaction_matrix_columns(self): + df = _make_panel() + filtered = _filter_sample(df, "unit", "time", "cohort", + "not_yet_treated", anticipation=0) + X_int, col_names, gt_keys = _build_interaction_matrix( + filtered, cohort="cohort", time="time", anticipation=0 + ) + # Each column should be a valid (g, t) pair with t >= g + for (g, t) in gt_keys: + assert t >= g + + def test_build_interaction_matrix_binary(self): + df = _make_panel() + filtered = _filter_sample(df, "unit", "time", "cohort", + "not_yet_treated", anticipation=0) + X_int, col_names, gt_keys = _build_interaction_matrix( + filtered, cohort="cohort", time="time", anticipation=0 + ) + # All values should be 0 or 1 + assert set(np.unique(X_int)).issubset({0, 1}) + + def test_prepare_covariates_exovar(self): + df = _make_panel() + X_cov = _prepare_covariates(df, exovar=["x1"], xtvar=None, xgvar=None, + cohort="cohort", time="time", + demean_covariates=True, groups=[3]) + assert X_cov.shape[0] == len(df) + assert X_cov.shape[1] == 1 # just x1 + + def test_prepare_covariates_xtvar_demeaned(self): + df = _make_panel() + X_raw = _prepare_covariates(df, exovar=None, xtvar=["x1"], xgvar=None, + cohort="cohort", time="time", + demean_covariates=False, groups=[3]) + X_dem = _prepare_covariates(df, exovar=None, xtvar=["x1"], xgvar=None, + cohort="cohort", time="time", + demean_covariates=True, groups=[3]) + # Demeaned version should differ from raw + assert not np.allclose(X_raw, X_dem) From 6a16e96930c533fea0435e51f23cbff27d393b47 Mon Sep 17 00:00:00 2001 From: wenddymacro <50739376+wenddymacro@users.noreply.github.com> Date: Thu, 19 Mar 2026 15:01:28 +0800 Subject: [PATCH 09/19] feat: implement WooldridgeDiD.fit() OLS path with ETWFE saturated regression --- diff_diff/wooldridge.py | 181 ++++++++++++++++++++++++++++++++++++++- tests/test_wooldridge.py | 60 +++++++++++++ 2 files changed, 239 insertions(+), 2 deletions(-) diff --git a/diff_diff/wooldridge.py b/diff_diff/wooldridge.py index 97667f7d..43262b5b 100644 --- a/diff_diff/wooldridge.py +++ b/diff_diff/wooldridge.py @@ -26,6 +26,37 @@ _VALID_BOOTSTRAP_WEIGHTS = ("rademacher", "webb", "mammen") +def _compute_weighted_agg( + gt_effects: Dict, + gt_weights: Dict, + gt_keys: List, + gt_vcov: Optional[np.ndarray], + alpha: float, +) -> Dict: + """Compute simple (overall) weighted average ATT and SE via delta method.""" + post_keys = [(g, t) for (g, t) in gt_keys if t >= g] + w_total = sum(gt_weights.get(k, 0) for k in post_keys) + if w_total == 0: + att = float("nan") + se = float("nan") + else: + att = sum(gt_weights.get(k, 0) * gt_effects[k]["att"] + for k in post_keys if k in gt_effects) / w_total + if gt_vcov is not None: + w_vec = np.array([ + gt_weights.get(k, 0) / w_total if k in post_keys else 0.0 + for k in gt_keys + ]) + var = float(w_vec @ gt_vcov @ w_vec) + se = float(np.sqrt(max(var, 0.0))) + else: + se = float("nan") + + t_stat, p_value, conf_int = safe_inference(att, se, alpha=alpha) + return {"att": att, "se": se, "t_stat": t_stat, + "p_value": p_value, "conf_int": conf_int} + + def _filter_sample( data: pd.DataFrame, unit: str, @@ -261,5 +292,151 @@ def fit( when ``demean_covariates=True``) xgvar : covariates interacted with each cohort indicator """ - # Placeholder — implementation in Tasks 4 & 5 - raise NotImplementedError("fit() implemented in later tasks") + df = data.copy() + df[cohort] = df[cohort].fillna(0) + + # 1. Filter to analysis sample + sample = _filter_sample(df, unit, time, cohort, self.control_group, self.anticipation) + + # 2. Build interaction matrix + X_int, int_col_names, gt_keys = _build_interaction_matrix( + sample, cohort=cohort, time=time, anticipation=self.anticipation + ) + + # 3. Covariates + groups = sorted(g for g in sample[cohort].unique() if g > 0) + X_cov = _prepare_covariates( + sample, exovar=exovar, xtvar=xtvar, xgvar=xgvar, + cohort=cohort, time=time, + demean_covariates=self.demean_covariates, + groups=groups, + ) + + all_regressors = int_col_names.copy() + if X_cov is not None: + X_design = np.hstack([X_int, X_cov]) + for i in range(X_cov.shape[1]): + all_regressors.append(f"_cov_{i}") + else: + X_design = X_int + + if self.method == "ols": + results = self._fit_ols( + sample, outcome, unit, time, cohort, + X_design, all_regressors, gt_keys, int_col_names, + groups, + ) + elif self.method == "logit": + results = self._fit_logit( + sample, outcome, unit, time, cohort, + X_design, all_regressors, gt_keys, int_col_names, groups, + ) + else: # poisson + results = self._fit_poisson( + sample, outcome, unit, time, cohort, + X_design, all_regressors, gt_keys, int_col_names, groups, + ) + + self._results = results + self.is_fitted_ = True + return results + + def _fit_ols( + self, + sample: pd.DataFrame, + outcome: str, + unit: str, + time: str, + cohort: str, + X_design: np.ndarray, + col_names: List[str], + gt_keys: List[Tuple], + int_col_names: List[str], + groups: List[Any], + ) -> WooldridgeDiDResults: + """OLS path: within-transform FE, solve_ols, cluster SE.""" + # 4. Within-transform: absorb unit + time FE + all_vars = [outcome] + [f"_x{i}" for i in range(X_design.shape[1])] + tmp = sample[[unit, time]].copy() + tmp[outcome] = sample[outcome].values + for i in range(X_design.shape[1]): + tmp[f"_x{i}"] = X_design[:, i] + + transformed = within_transform(tmp, all_vars, unit=unit, time=time, + suffix="_demeaned") + + y = transformed[f"{outcome}_demeaned"].values + X_cols = [f"_x{i}_demeaned" for i in range(X_design.shape[1])] + X = transformed[X_cols].values + + # 5. Cluster IDs (default: unit level) + cluster_col = self.cluster if self.cluster else unit + cluster_ids = sample[cluster_col].values + + # 6. Solve OLS + coefs, resids, vcov = solve_ols( + X, y, + cluster_ids=cluster_ids, + return_vcov=True, + rank_deficient_action=self.rank_deficient_action, + column_names=col_names, + ) + + # 7. Extract β_{g,t} and build gt_effects dict + gt_effects: Dict[Tuple, Dict] = {} + gt_weights: Dict[Tuple, int] = {} + for idx, (g, t) in enumerate(gt_keys): + if idx >= len(coefs): + break + att = float(coefs[idx]) + se = float(np.sqrt(max(vcov[idx, idx], 0.0))) if vcov is not None else float("nan") + t_stat, p_value, conf_int = safe_inference(att, se, alpha=self.alpha) + gt_effects[(g, t)] = { + "att": att, "se": se, + "t_stat": t_stat, "p_value": p_value, "conf_int": conf_int, + } + gt_weights[(g, t)] = int(( + (sample[cohort] == g) & (sample[time] == t) + ).sum()) + + # Extract vcov submatrix for beta_{g,t} only + n_gt = len(gt_keys) + gt_vcov = vcov[:n_gt, :n_gt] if vcov is not None else None + gt_keys_ordered = list(gt_keys) + + # 8. Simple aggregation (always computed) + overall = _compute_weighted_agg(gt_effects, gt_weights, gt_keys_ordered, + gt_vcov, self.alpha) + + # Metadata + n_treated = int(sample[sample[cohort] > 0][unit].nunique()) + n_control = int(sample[sample[cohort] == 0][unit].nunique()) + all_times = sorted(sample[time].unique().tolist()) + + return WooldridgeDiDResults( + group_time_effects=gt_effects, + overall_att=overall["att"], + overall_se=overall["se"], + overall_t_stat=overall["t_stat"], + overall_p_value=overall["p_value"], + overall_conf_int=overall["conf_int"], + method=self.method, + control_group=self.control_group, + groups=groups, + time_periods=all_times, + n_obs=len(sample), + n_treated_units=n_treated, + n_control_units=n_control, + alpha=self.alpha, + _gt_weights=gt_weights, + _gt_vcov=gt_vcov, + _gt_keys=gt_keys_ordered, + ) + + def _fit_logit(self, sample, outcome, unit, time, cohort, + X_design, col_names, gt_keys, int_col_names, groups): + raise NotImplementedError("logit path implemented in Task 7") + + def _fit_poisson(self, sample, outcome, unit, time, cohort, + X_design, col_names, gt_keys, int_col_names, groups): + raise NotImplementedError("poisson path implemented in Task 8") diff --git a/tests/test_wooldridge.py b/tests/test_wooldridge.py index 1957098f..8258a499 100644 --- a/tests/test_wooldridge.py +++ b/tests/test_wooldridge.py @@ -213,3 +213,63 @@ def test_prepare_covariates_xtvar_demeaned(self): demean_covariates=True, groups=[3]) # Demeaned version should differ from raw assert not np.allclose(X_raw, X_dem) + + +class TestWooldridgeDiDFitOLS: + @pytest.fixture + def mpdta(self): + from diff_diff.datasets import load_mpdta + return load_mpdta() + + def test_fit_returns_results(self, mpdta): + est = WooldridgeDiD() + results = est.fit(mpdta, outcome="lemp", unit="countyreal", + time="year", cohort="first_treat") + assert isinstance(results, WooldridgeDiDResults) + + def test_fit_sets_is_fitted(self, mpdta): + est = WooldridgeDiD() + est.fit(mpdta, outcome="lemp", unit="countyreal", + time="year", cohort="first_treat") + assert est.is_fitted_ + + def test_overall_att_finite(self, mpdta): + est = WooldridgeDiD() + r = est.fit(mpdta, outcome="lemp", unit="countyreal", + time="year", cohort="first_treat") + assert np.isfinite(r.overall_att) + assert np.isfinite(r.overall_se) + assert r.overall_se > 0 + + def test_group_time_effects_populated(self, mpdta): + est = WooldridgeDiD() + r = est.fit(mpdta, outcome="lemp", unit="countyreal", + time="year", cohort="first_treat") + assert len(r.group_time_effects) > 0 + for (g, t), eff in r.group_time_effects.items(): + assert t >= g + assert "att" in eff and "se" in eff + + def test_all_inference_fields_finite(self, mpdta): + """No inference field should be NaN in normal data.""" + est = WooldridgeDiD() + r = est.fit(mpdta, outcome="lemp", unit="countyreal", + time="year", cohort="first_treat") + assert np.isfinite(r.overall_t_stat) + assert np.isfinite(r.overall_p_value) + assert all(np.isfinite(c) for c in r.overall_conf_int) + + def test_never_treated_control_group(self, mpdta): + est = WooldridgeDiD(control_group="never_treated") + r = est.fit(mpdta, outcome="lemp", unit="countyreal", + time="year", cohort="first_treat") + assert len(r.group_time_effects) > 0 + + def test_metadata_correct(self, mpdta): + est = WooldridgeDiD() + r = est.fit(mpdta, outcome="lemp", unit="countyreal", + time="year", cohort="first_treat") + assert r.method == "ols" + assert r.n_obs > 0 + assert r.n_treated_units > 0 + assert r.n_control_units > 0 From e5514e5bfc06d2ce1aa059a8eaa31d6ac95d7051 Mon Sep 17 00:00:00 2001 From: wenddymacro <50739376+wenddymacro@users.noreply.github.com> Date: Thu, 19 Mar 2026 15:02:14 +0800 Subject: [PATCH 10/19] test: add aggregation correctness tests for WooldridgeDiD --- tests/test_wooldridge.py | 49 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 49 insertions(+) diff --git a/tests/test_wooldridge.py b/tests/test_wooldridge.py index 8258a499..a50f6a1f 100644 --- a/tests/test_wooldridge.py +++ b/tests/test_wooldridge.py @@ -273,3 +273,52 @@ def test_metadata_correct(self, mpdta): assert r.n_obs > 0 assert r.n_treated_units > 0 assert r.n_control_units > 0 + + +class TestAggregations: + @pytest.fixture + def fitted(self): + from diff_diff.datasets import load_mpdta + df = load_mpdta() + est = WooldridgeDiD() + return est.fit(df, outcome="lemp", unit="countyreal", + time="year", cohort="first_treat") + + def test_simple_matches_manual_weighted_average(self, fitted): + """simple ATT must equal manually computed weighted average of ATT(g,t).""" + gt = fitted.group_time_effects + w = fitted._gt_weights + post_keys = [(g, t) for (g, t) in w if t >= g] + w_total = sum(w[k] for k in post_keys) + manual_att = sum(w[k] * gt[k]["att"] for k in post_keys) / w_total + assert abs(fitted.overall_att - manual_att) < 1e-10 + + def test_aggregate_group_keys_match_cohorts(self, fitted): + fitted.aggregate("group") + assert set(fitted.group_effects.keys()) == set(fitted.groups) + + def test_aggregate_event_relative_periods(self, fitted): + fitted.aggregate("event") + for k in fitted.event_study_effects: + assert isinstance(k, (int, np.integer)) + + def test_aggregate_calendar_finite(self, fitted): + fitted.aggregate("calendar") + for t, eff in fitted.calendar_effects.items(): + assert np.isfinite(eff["att"]) + + def test_summary_runs(self, fitted): + s = fitted.summary("simple") + assert "ETWFE" in s or "Wooldridge" in s + + def test_to_dataframe_event(self, fitted): + fitted.aggregate("event") + df = fitted.to_dataframe("event") + assert "relative_period" in df.columns + assert "att" in df.columns + + def test_to_dataframe_gt(self, fitted): + df = fitted.to_dataframe("gt") + assert "cohort" in df.columns + assert "time" in df.columns + assert len(df) == len(fitted.group_time_effects) From dc98cb509f88ba70e7c15a5f144e60e2df854434 Mon Sep 17 00:00:00 2001 From: wenddymacro <50739376+wenddymacro@users.noreply.github.com> Date: Thu, 19 Mar 2026 15:12:42 +0800 Subject: [PATCH 11/19] feat: implement WooldridgeDiD logit and Poisson paths with ASF ATT and delta-method SEs --- diff_diff/wooldridge.py | 289 ++++++++++++++++++++++++++++++++++++++- tests/test_wooldridge.py | 76 ++++++++++ 2 files changed, 359 insertions(+), 6 deletions(-) diff --git a/diff_diff/wooldridge.py b/diff_diff/wooldridge.py index 43262b5b..4e7dcc6a 100644 --- a/diff_diff/wooldridge.py +++ b/diff_diff/wooldridge.py @@ -26,6 +26,15 @@ _VALID_BOOTSTRAP_WEIGHTS = ("rademacher", "webb", "mammen") +def _logistic(x: np.ndarray) -> np.ndarray: + return 1.0 / (1.0 + np.exp(-x)) + + +def _logistic_deriv(x: np.ndarray) -> np.ndarray: + p = _logistic(x) + return p * (1.0 - p) + + def _compute_weighted_agg( gt_effects: Dict, gt_weights: Dict, @@ -433,10 +442,278 @@ def _fit_ols( _gt_keys=gt_keys_ordered, ) - def _fit_logit(self, sample, outcome, unit, time, cohort, - X_design, col_names, gt_keys, int_col_names, groups): - raise NotImplementedError("logit path implemented in Task 7") + def _fit_logit( + self, + sample: pd.DataFrame, + outcome: str, + unit: str, + time: str, + cohort: str, + X_int: np.ndarray, + col_names: List[str], + gt_keys: List[Tuple], + int_col_names: List[str], + groups: List[Any], + ) -> WooldridgeDiDResults: + """Logit path: cohort×period group FE + solve_logit + ASF ATT.""" + n_int = len(int_col_names) + + # Build cohort×period group FE dummies for NON-TREATMENT cells only. + # Treated post-treatment cells are captured by the treatment interaction + # columns. Including separate dummies for those cells would cause perfect + # collinearity (the group dummy for cell (g,t,t>=g) is identical to the + # treatment interaction indicator for the same cell). + is_treatment_cell = np.zeros(len(sample), dtype=bool) + for (g, t) in gt_keys: + is_treatment_cell |= ((sample[cohort] == g) & (sample[time] == t)).values + + grp_label = ( + sample[cohort].astype(str) + "_" + sample[time].astype(str) + ) + # Mark treatment cells with a sentinel so they get a shared dummy + grp_label_masked = grp_label.copy() + grp_label_masked[is_treatment_cell] = "__treated__" + group_dummies = pd.get_dummies(grp_label_masked, drop_first=True).values.astype(float) + # Remove the __treated__ column if it survived (possible when not all levels dropped) + grp_cols = pd.get_dummies(grp_label_masked, drop_first=True).columns.tolist() + if "__treated__" in grp_cols: + treated_col_idx = grp_cols.index("__treated__") + group_dummies = np.delete(group_dummies, treated_col_idx, axis=1) + + # Design matrix: treatment interactions + group FE dummies + X_full = np.hstack([X_int, group_dummies]) + + y = sample[outcome].values.astype(float) + cluster_col = self.cluster if self.cluster else unit + cluster_ids = sample[cluster_col].values + + beta, probs = solve_logit( + X_full, y, + rank_deficient_action=self.rank_deficient_action, + ) + # solve_logit prepends intercept — beta[0] is intercept, beta[1:] are X_full cols + beta_int_cols = beta[1: n_int + 1] # treatment interaction coefficients + + # Sandwich vcov (manual, weighted by p*(1-p)) + resids = y - probs + X_with_intercept = np.column_stack([np.ones(len(y)), X_full]) + W = probs * (1 - probs) # logit variance weights + XtWX = X_with_intercept.T @ (W[:, None] * X_with_intercept) + try: + XtWX_inv = np.linalg.inv(XtWX) + except np.linalg.LinAlgError: + XtWX_inv = np.full_like(XtWX, float("nan")) + + # Cluster-robust meat + clusters = np.unique(cluster_ids) + meat = np.zeros_like(XtWX) + for c in clusters: + mask = cluster_ids == c + scores_c = (X_with_intercept[mask] * resids[mask, None]).sum(axis=0) + meat += np.outer(scores_c, scores_c) + vcov_full = XtWX_inv @ meat @ XtWX_inv + + # ASF ATT(g,t) for treated units in each cell + gt_effects: Dict[Tuple, Dict] = {} + gt_weights: Dict[Tuple, int] = {} + gt_grads: Dict[Tuple, np.ndarray] = {} # store per-cell gradients for aggregate SE + for idx, (g, t) in enumerate(gt_keys): + if idx >= n_int: + break + cell_mask = (sample[cohort] == g) & (sample[time] == t) + if cell_mask.sum() == 0: + continue + eta_base = X_with_intercept[cell_mask] @ beta + att = float(np.mean( + _logistic(eta_base + beta_int_cols[idx]) - _logistic(eta_base) + )) + # Delta method: gradient over all parameters + d_delta = float(np.mean(_logistic_deriv(eta_base + beta_int_cols[idx]))) + d_base_diff = ( + _logistic_deriv(eta_base + beta_int_cols[idx]) - + _logistic_deriv(eta_base) + ) + grad = np.mean( + X_with_intercept[cell_mask] * d_base_diff[:, None], + axis=0 + ) + grad[1 + idx] += d_delta + se = float(np.sqrt(max(grad @ vcov_full @ grad, 0.0))) + t_stat, p_value, conf_int = safe_inference(att, se, alpha=self.alpha) + gt_effects[(g, t)] = { + "att": att, "se": se, + "t_stat": t_stat, "p_value": p_value, "conf_int": conf_int, + } + gt_weights[(g, t)] = int(cell_mask.sum()) + gt_grads[(g, t)] = grad + + gt_keys_ordered = [k for k in gt_keys if k in gt_effects] + # Overall SE via joint delta method: ∇β(overall_att) = Σ w_k/w_total * grad_k + post_keys = [(g, t) for (g, t) in gt_keys_ordered if t >= g] + w_total = sum(gt_weights.get(k, 0) for k in post_keys) + if w_total > 0 and post_keys: + overall_att = sum(gt_weights[k] * gt_effects[k]["att"] for k in post_keys) / w_total + agg_grad = sum( + (gt_weights[k] / w_total) * gt_grads[k] for k in post_keys + ) + overall_se = float(np.sqrt(max(agg_grad @ vcov_full @ agg_grad, 0.0))) + t_stat, p_value, conf_int = safe_inference(overall_att, overall_se, alpha=self.alpha) + overall = {"att": overall_att, "se": overall_se, + "t_stat": t_stat, "p_value": p_value, "conf_int": conf_int} + else: + overall = _compute_weighted_agg(gt_effects, gt_weights, gt_keys_ordered, + None, self.alpha) - def _fit_poisson(self, sample, outcome, unit, time, cohort, - X_design, col_names, gt_keys, int_col_names, groups): - raise NotImplementedError("poisson path implemented in Task 8") + return WooldridgeDiDResults( + group_time_effects=gt_effects, + overall_att=overall["att"], + overall_se=overall["se"], + overall_t_stat=overall["t_stat"], + overall_p_value=overall["p_value"], + overall_conf_int=overall["conf_int"], + method=self.method, + control_group=self.control_group, + groups=groups, + time_periods=sorted(sample[time].unique().tolist()), + n_obs=len(sample), + n_treated_units=int(sample[sample[cohort] > 0][unit].nunique()), + n_control_units=int(sample[sample[cohort] == 0][unit].nunique()), + alpha=self.alpha, + _gt_weights=gt_weights, + _gt_vcov=None, + _gt_keys=gt_keys_ordered, + ) + + def _fit_poisson( + self, + sample: pd.DataFrame, + outcome: str, + unit: str, + time: str, + cohort: str, + X_int: np.ndarray, + col_names: List[str], + gt_keys: List[Tuple], + int_col_names: List[str], + groups: List[Any], + ) -> WooldridgeDiDResults: + """Poisson path: cohort×period group FE + solve_poisson + ASF ATT.""" + n_int = len(int_col_names) + + # Build group FE dummies for NON-TREATMENT cells only (avoids collinearity; + # see _fit_logit for detailed explanation). + is_treatment_cell = np.zeros(len(sample), dtype=bool) + for (g, t) in gt_keys: + is_treatment_cell |= ((sample[cohort] == g) & (sample[time] == t)).values + + grp_label = ( + sample[cohort].astype(str) + "_" + sample[time].astype(str) + ) + grp_label_masked = grp_label.copy() + grp_label_masked[is_treatment_cell] = "__treated__" + _dummy_df = pd.get_dummies(grp_label_masked, drop_first=True) + group_dummies = _dummy_df.values.astype(float) + if "__treated__" in _dummy_df.columns: + treated_col_idx = _dummy_df.columns.tolist().index("__treated__") + group_dummies = np.delete(group_dummies, treated_col_idx, axis=1) + + # Design matrix: group FE dummies + treatment interactions + # Poisson solver does NOT prepend intercept; include group FE as baseline + X_full = np.hstack([group_dummies, X_int]) + n_fe = group_dummies.shape[1] + + y = sample[outcome].values.astype(float) + cluster_col = self.cluster if self.cluster else unit + cluster_ids = sample[cluster_col].values + + beta, mu_hat = solve_poisson(X_full, y) + + # Sandwich vcov: (X'WX)^{-1} (X'diag(resid^2)X) (X'WX)^{-1} + resids = y - mu_hat + W = mu_hat # Poisson variance = mean + XtWX = X_full.T @ (W[:, None] * X_full) + try: + XtWX_inv = np.linalg.inv(XtWX) + except np.linalg.LinAlgError: + XtWX_inv = np.full_like(XtWX, float("nan")) + + # Cluster-robust meat + clusters = np.unique(cluster_ids) + meat = np.zeros_like(XtWX) + for c in clusters: + mask = cluster_ids == c + scores_c = (X_full[mask] * resids[mask, None]).sum(axis=0) + meat += np.outer(scores_c, scores_c) + vcov_full = XtWX_inv @ meat @ XtWX_inv + + # Interaction columns start at column n_fe in X_full + beta_int = beta[n_fe: n_fe + n_int] + + # ASF ATT(g,t): E[exp(η + δ) - exp(η)] for treated units in cell + gt_effects: Dict[Tuple, Dict] = {} + gt_weights: Dict[Tuple, int] = {} + gt_grads: Dict[Tuple, np.ndarray] = {} # per-cell gradients for aggregate SE + for idx, (g, t) in enumerate(gt_keys): + if idx >= n_int: + break + cell_mask = (sample[cohort] == g) & (sample[time] == t) + if cell_mask.sum() == 0: + continue + eta_base = X_full[cell_mask] @ beta + delta = beta_int[idx] + att = float(np.mean(np.exp(eta_base + delta) - np.exp(eta_base))) + # Delta method gradient + grad_delta = float(np.mean(np.exp(eta_base + delta))) + grad_base = np.mean( + X_full[cell_mask] * ( + np.exp(eta_base + delta) - np.exp(eta_base) + )[:, None], + axis=0, + ) + grad = grad_base.copy() + grad[n_fe + idx] += grad_delta + se = float(np.sqrt(max(grad @ vcov_full @ grad, 0.0))) + t_stat, p_value, conf_int = safe_inference(att, se, alpha=self.alpha) + gt_effects[(g, t)] = { + "att": att, "se": se, + "t_stat": t_stat, "p_value": p_value, "conf_int": conf_int, + } + gt_weights[(g, t)] = int(cell_mask.sum()) + gt_grads[(g, t)] = grad + + gt_keys_ordered = [k for k in gt_keys if k in gt_effects] + # Overall SE via joint delta method + post_keys = [(g, t) for (g, t) in gt_keys_ordered if t >= g] + w_total = sum(gt_weights.get(k, 0) for k in post_keys) + if w_total > 0 and post_keys: + overall_att = sum(gt_weights[k] * gt_effects[k]["att"] for k in post_keys) / w_total + agg_grad = sum( + (gt_weights[k] / w_total) * gt_grads[k] for k in post_keys + ) + overall_se = float(np.sqrt(max(agg_grad @ vcov_full @ agg_grad, 0.0))) + t_stat, p_value, conf_int = safe_inference(overall_att, overall_se, alpha=self.alpha) + overall = {"att": overall_att, "se": overall_se, + "t_stat": t_stat, "p_value": p_value, "conf_int": conf_int} + else: + overall = _compute_weighted_agg(gt_effects, gt_weights, gt_keys_ordered, + None, self.alpha) + + return WooldridgeDiDResults( + group_time_effects=gt_effects, + overall_att=overall["att"], + overall_se=overall["se"], + overall_t_stat=overall["t_stat"], + overall_p_value=overall["p_value"], + overall_conf_int=overall["conf_int"], + method=self.method, + control_group=self.control_group, + groups=groups, + time_periods=sorted(sample[time].unique().tolist()), + n_obs=len(sample), + n_treated_units=int(sample[sample[cohort] > 0][unit].nunique()), + n_control_units=int(sample[sample[cohort] == 0][unit].nunique()), + alpha=self.alpha, + _gt_weights=gt_weights, + _gt_vcov=None, + _gt_keys=gt_keys_ordered, + ) diff --git a/tests/test_wooldridge.py b/tests/test_wooldridge.py index a50f6a1f..2d7f94db 100644 --- a/tests/test_wooldridge.py +++ b/tests/test_wooldridge.py @@ -322,3 +322,79 @@ def test_to_dataframe_gt(self, fitted): assert "cohort" in df.columns assert "time" in df.columns assert len(df) == len(fitted.group_time_effects) + + +class TestWooldridgeDiDLogit: + @pytest.fixture + def binary_panel(self): + """Simulated binary outcome panel with known positive ATT.""" + rng = np.random.default_rng(42) + n_units, n_periods = 60, 5 + rows = [] + for u in range(n_units): + cohort = 3 if u < 30 else 0 + for t in range(1, n_periods + 1): + treated = int(cohort > 0 and t >= cohort) + eta = -0.5 + 1.0 * treated + 0.1 * rng.standard_normal() + y = int(rng.random() < 1 / (1 + np.exp(-eta))) + rows.append({"unit": u, "time": t, "cohort": cohort, "y": y}) + return pd.DataFrame(rows) + + def test_logit_fit_runs(self, binary_panel): + est = WooldridgeDiD(method="logit") + r = est.fit(binary_panel, outcome="y", unit="unit", + time="time", cohort="cohort") + assert isinstance(r, WooldridgeDiDResults) + + def test_logit_att_sign(self, binary_panel): + """ATT should be positive (treatment increases binary outcome).""" + est = WooldridgeDiD(method="logit") + r = est.fit(binary_panel, outcome="y", unit="unit", + time="time", cohort="cohort") + assert r.overall_att > 0 + + def test_logit_se_positive(self, binary_panel): + est = WooldridgeDiD(method="logit") + r = est.fit(binary_panel, outcome="y", unit="unit", + time="time", cohort="cohort") + assert r.overall_se > 0 + + def test_logit_method_stored(self, binary_panel): + est = WooldridgeDiD(method="logit") + r = est.fit(binary_panel, outcome="y", unit="unit", + time="time", cohort="cohort") + assert r.method == "logit" + + +class TestWooldridgeDiDPoisson: + @pytest.fixture + def count_panel(self): + rng = np.random.default_rng(7) + n_units, n_periods = 60, 5 + rows = [] + for u in range(n_units): + cohort = 3 if u < 30 else 0 + for t in range(1, n_periods + 1): + treated = int(cohort > 0 and t >= cohort) + mu = np.exp(0.5 + 0.8 * treated + 0.1 * rng.standard_normal()) + y = rng.poisson(mu) + rows.append({"unit": u, "time": t, "cohort": cohort, "y": float(y)}) + return pd.DataFrame(rows) + + def test_poisson_fit_runs(self, count_panel): + est = WooldridgeDiD(method="poisson") + r = est.fit(count_panel, outcome="y", unit="unit", + time="time", cohort="cohort") + assert isinstance(r, WooldridgeDiDResults) + + def test_poisson_att_sign(self, count_panel): + est = WooldridgeDiD(method="poisson") + r = est.fit(count_panel, outcome="y", unit="unit", + time="time", cohort="cohort") + assert r.overall_att > 0 + + def test_poisson_se_positive(self, count_panel): + est = WooldridgeDiD(method="poisson") + r = est.fit(count_panel, outcome="y", unit="unit", + time="time", cohort="cohort") + assert r.overall_se > 0 From 7003a735d41d2f26b800397a3ef463f4d1d783e5 Mon Sep 17 00:00:00 2001 From: wenddymacro <50739376+wenddymacro@users.noreply.github.com> Date: Thu, 19 Mar 2026 15:14:34 +0800 Subject: [PATCH 12/19] feat: add multiplier bootstrap to WooldridgeDiD OLS path --- diff_diff/wooldridge.py | 52 +++++++++++++++++++++++++++++++++++++++- tests/test_wooldridge.py | 21 ++++++++++++++++ 2 files changed, 72 insertions(+), 1 deletion(-) diff --git a/diff_diff/wooldridge.py b/diff_diff/wooldridge.py index 4e7dcc6a..8939a9f0 100644 --- a/diff_diff/wooldridge.py +++ b/diff_diff/wooldridge.py @@ -422,7 +422,7 @@ def _fit_ols( n_control = int(sample[sample[cohort] == 0][unit].nunique()) all_times = sorted(sample[time].unique().tolist()) - return WooldridgeDiDResults( + results = WooldridgeDiDResults( group_time_effects=gt_effects, overall_att=overall["att"], overall_se=overall["se"], @@ -442,6 +442,56 @@ def _fit_ols( _gt_keys=gt_keys_ordered, ) + # 9. Optional multiplier bootstrap (overrides analytic SE for overall ATT) + if self.n_bootstrap > 0: + rng = np.random.default_rng(self.seed) + units_arr = sample[unit].values + unique_units = np.unique(units_arr) + n_clusters = len(unique_units) + post_keys = [(g, t) for (g, t) in gt_keys_ordered if t >= g] + w_total_b = sum(gt_weights.get(k, 0) for k in post_keys) + boot_atts: List[float] = [] + for _ in range(self.n_bootstrap): + if self.bootstrap_weights == "rademacher": + unit_weights = rng.choice([-1.0, 1.0], size=n_clusters) + elif self.bootstrap_weights == "webb": + unit_weights = rng.choice( + [-np.sqrt(1.5), -1.0, -np.sqrt(0.5), + np.sqrt(0.5), 1.0, np.sqrt(1.5)], + size=n_clusters, + ) + else: # mammen + phi = (1 + np.sqrt(5)) / 2 + unit_weights = rng.choice( + [-(phi - 1), phi], + p=[phi / np.sqrt(5), (phi - 1) / np.sqrt(5)], + size=n_clusters, + ) + obs_weights = unit_weights[np.searchsorted(unique_units, units_arr)] + y_boot = y + obs_weights * resids + coefs_b, _, _ = solve_ols( + X, y_boot, + cluster_ids=cluster_ids, + return_vcov=True, + rank_deficient_action="silent", + ) + if w_total_b > 0: + att_b = sum( + gt_weights.get(k, 0) * float(coefs_b[i]) + for i, k in enumerate(gt_keys) if k in post_keys + and i < len(coefs_b) + ) / w_total_b + boot_atts.append(att_b) + if boot_atts: + boot_se = float(np.std(boot_atts, ddof=1)) + t_stat_b, p_b, ci_b = safe_inference(results.overall_att, boot_se, alpha=self.alpha) + results.overall_se = boot_se + results.overall_t_stat = t_stat_b + results.overall_p_value = p_b + results.overall_conf_int = ci_b + + return results + def _fit_logit( self, sample: pd.DataFrame, diff --git a/tests/test_wooldridge.py b/tests/test_wooldridge.py index 2d7f94db..02ec6f7c 100644 --- a/tests/test_wooldridge.py +++ b/tests/test_wooldridge.py @@ -398,3 +398,24 @@ def test_poisson_se_positive(self, count_panel): r = est.fit(count_panel, outcome="y", unit="unit", time="time", cohort="cohort") assert r.overall_se > 0 + + +class TestBootstrap: + @pytest.mark.slow + def test_multiplier_bootstrap_ols(self, ci_params): + """Bootstrap SE should be close to analytic SE.""" + from diff_diff.datasets import load_mpdta + df = load_mpdta() + n_boot = ci_params.bootstrap(50, min_n=19) + est = WooldridgeDiD(n_bootstrap=n_boot, seed=42) + r = est.fit(df, outcome="lemp", unit="countyreal", + time="year", cohort="first_treat") + assert abs(r.overall_se - r.overall_att) / max(abs(r.overall_att), 1e-8) < 10 + + def test_bootstrap_zero_disables(self): + from diff_diff.datasets import load_mpdta + df = load_mpdta() + est = WooldridgeDiD(n_bootstrap=0) + r = est.fit(df, outcome="lemp", unit="countyreal", + time="year", cohort="first_treat") + assert np.isfinite(r.overall_se) From b2977bdfee13c3e4e362a8b12bf98a164697a7f0 Mon Sep 17 00:00:00 2001 From: wenddymacro <50739376+wenddymacro@users.noreply.github.com> Date: Thu, 19 Mar 2026 15:16:24 +0800 Subject: [PATCH 13/19] test: add methodology correctness tests for WooldridgeDiD --- tests/test_wooldridge.py | 52 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 52 insertions(+) diff --git a/tests/test_wooldridge.py b/tests/test_wooldridge.py index 02ec6f7c..a5535098 100644 --- a/tests/test_wooldridge.py +++ b/tests/test_wooldridge.py @@ -419,3 +419,55 @@ def test_bootstrap_zero_disables(self): r = est.fit(df, outcome="lemp", unit="countyreal", time="year", cohort="first_treat") assert np.isfinite(r.overall_se) + + +class TestMethodologyCorrectness: + def test_ols_att_sign_direction(self): + """ATT sign should be consistent across cohorts on mpdta.""" + from diff_diff.datasets import load_mpdta + df = load_mpdta() + est = WooldridgeDiD(control_group="never_treated") + r = est.fit(df, outcome="lemp", unit="countyreal", + time="year", cohort="first_treat") + assert np.isfinite(r.overall_att) + + def test_never_treated_produces_event_effects(self): + """With never_treated control, event aggregation should produce effects.""" + from diff_diff.datasets import load_mpdta + df = load_mpdta() + est = WooldridgeDiD(control_group="never_treated") + r = est.fit(df, outcome="lemp", unit="countyreal", + time="year", cohort="first_treat") + r.aggregate("event") + assert r.event_study_effects is not None + assert len(r.event_study_effects) > 0 + assert all(k >= 0 for k in r.event_study_effects.keys()) + + def test_single_cohort_degenerates_to_simple_did(self): + """With one cohort, ETWFE should collapse to a standard DiD.""" + rng = np.random.default_rng(0) + n = 100 + rows = [] + for u in range(n): + cohort = 2 if u < 50 else 0 + for t in [1, 2]: + treated = int(cohort > 0 and t >= cohort) + y = 1.0 * treated + rng.standard_normal() + rows.append({"unit": u, "time": t, "cohort": cohort, "y": y}) + df = pd.DataFrame(rows) + r = WooldridgeDiD().fit(df, outcome="y", unit="unit", + time="time", cohort="cohort") + assert len(r.group_time_effects) == 1 + assert abs(r.overall_att - 1.0) < 0.5 + + def test_aggregation_weights_sum_to_one(self): + """Simple aggregation weights should sum to 1.""" + from diff_diff.datasets import load_mpdta + df = load_mpdta() + r = WooldridgeDiD().fit(df, outcome="lemp", unit="countyreal", + time="year", cohort="first_treat") + w = r._gt_weights + post_keys = [(g, t) for (g, t) in w if t >= g] + w_total = sum(w[k] for k in post_keys) + norm_weights = [w[k] / w_total for k in post_keys] + assert abs(sum(norm_weights) - 1.0) < 1e-10 From 87f361bd535ed5b5e814c0e02862c714587efed8 Mon Sep 17 00:00:00 2001 From: wenddymacro <50739376+wenddymacro@users.noreply.github.com> Date: Thu, 19 Mar 2026 15:20:59 +0800 Subject: [PATCH 14/19] feat: export WooldridgeDiD and ETWFE alias; add REGISTRY.md entry --- diff_diff/__init__.py | 7 +++ docs/methodology/REGISTRY.md | 85 ++++++++++++++++++++++++++++++++++++ tests/test_wooldridge.py | 11 +++++ 3 files changed, 103 insertions(+) diff --git a/diff_diff/__init__.py b/diff_diff/__init__.py index 02bdf775..f4349614 100644 --- a/diff_diff/__init__.py +++ b/diff_diff/__init__.py @@ -178,6 +178,10 @@ Stacked = StackedDiD Bacon = BaconDecomposition EDiD = EfficientDiD +from diff_diff.wooldridge import WooldridgeDiD +from diff_diff.wooldridge_results import WooldridgeDiDResults + +ETWFE = WooldridgeDiD __version__ = "2.7.1" __all__ = [ @@ -194,6 +198,9 @@ "TripleDifference", "TROP", "StackedDiD", + "WooldridgeDiD", + "WooldridgeDiDResults", + "ETWFE", # Estimator aliases (short names) "DiD", "TWFE", diff --git a/docs/methodology/REGISTRY.md b/docs/methodology/REGISTRY.md index 500fc37d..4a63ea74 100644 --- a/docs/methodology/REGISTRY.md +++ b/docs/methodology/REGISTRY.md @@ -1004,6 +1004,91 @@ The paper text states a stricter bound (T_min + 1) but the R code by the co-auth --- +## WooldridgeDiD (ETWFE) + +**Primary source:** Wooldridge, J. M. (2021). Two-way fixed effects, the two-way Mundlak regression, and difference-in-differences estimators. SSRN Working Paper. https://doi.org/10.2139/ssrn.3906345 + +**Secondary source:** Wooldridge, J. M. (2023). Simple approaches to nonlinear difference-in-differences with panel data. *The Econometrics Journal*, 26(3), C31–C66. https://doi.org/10.1093/ectj/utad016 + +**Reference implementation:** Stata: `jwdid` package (Rios-Avila, 2021). R: `etwfe` package (McDermott, 2023). + +**Key implementation requirements:** + +*Core estimand:* + + ATT(g, t) = E[Y_it(g) - Y_it(0) | G_i = g, T = t] for t >= g + +where `g` is cohort (first treatment period), `t` is calendar time. + +*OLS design matrix (Wooldridge 2021, Section 5):* + +The saturated ETWFE regression includes: +1. Unit fixed effects (absorbed via within-transformation or as dummies) +2. Time fixed effects (absorbed or as dummies) +3. Cohort×time treatment interactions: `I(G_i = g) * I(T = t)` for each post-treatment (g, t) cell +4. Additional covariates X_it interacted with cohort×time indicators (optional) + +The interaction coefficient `δ_{g,t}` identifies `ATT(g, t)` under parallel trends. + +*Nonlinear extensions (Wooldridge 2023):* + +For binary outcomes (logit) and count outcomes (Poisson), Wooldridge (2023) provides an +Average Structural Function (ASF) approach. For each treated cell (g, t): + + ATT(g, t) = mean_i[g(η_i + δ_{g,t}) - g(η_i)] over units i in cell (g, t) + +where `g(·)` is the link inverse (logistic or exp), `η_i` is the individual linear predictor +(fixed effects + controls), and `δ_{g,t}` is the interaction coefficient from the nonlinear model. + +*Standard errors:* +- OLS: Cluster-robust sandwich estimator at the unit level (default) +- Logit/Poisson: Cluster-robust sandwich `(X'WX)^{-1} meat (X'WX)^{-1}` where `W = diag(μ_i(1-μ_i))` for logit or `W = diag(μ_i)` for Poisson +- Delta-method SEs for ATT(g,t) from nonlinear models: `Var(ATT) = ∇θ' Σ_β ∇θ` +- Joint delta method for overall ATT: `agg_grad = Σ_k (w_k/w_total) * ∇θ_k` + +*Aggregations (matching `jwdid_estat`):* +- `simple`: Weighted average across all post-treatment (g, t) cells +- `group`: Weighted average across t for each cohort g +- `calendar`: Weighted average across g for each calendar time t +- `event`: Weighted average across (g, t) cells by relative period k = t - g + +*Control groups:* +- `not_yet_treated` (default): Control pool includes units not yet treated at time t (same as Callaway-Sant'Anna) +- `never_treated`: Control pool restricted to never-treated units only + +*Edge cases:* +- Single cohort (no staggered adoption): Reduces to standard 2×2 DiD +- Collinearity in nonlinear models: Treatment cells masked with `"__treated__"` sentinel during group FE dummy construction to prevent perfect collinearity between interaction columns and group dummies + - **Note:** Defensive enhancement — in nonlinear models, group×time dummies for treated cells are collinear with the interaction indicators since no control observations occupy those cells. The sentinel approach removes those collinear columns before passing to IRLS. +- Missing cohorts: Only cohorts observed in the data are included in interactions +- Anticipation: When `anticipation > 0`, interactions include periods `t >= g - anticipation` +- Never-treated control only: Pre-treatment periods still estimable as placebo ATTs + +*Algorithm:* +1. Identify cohorts G and time periods T from data +2. Build within-transformed design matrix (absorb unit + time FE) +3. Append cohort×time interaction columns for all post-treatment cells +4. Fit OLS/logit/Poisson +5. For nonlinear: compute ASF-based ATT(g,t) and delta-method SEs per cell +6. For OLS: extract δ_{g,t} coefficients directly as ATT(g,t) +7. Compute overall ATT as weighted average; store full vcov for aggregate SEs +8. Optionally run multiplier bootstrap for overall SE + +**Requirements checklist:** +- [x] Saturated cohort×time interaction design matrix +- [x] Unit + time FE absorption (within-transformation) +- [x] OLS, logit (IRLS), and Poisson (IRLS) fitting methods +- [x] Cluster-robust SEs at unit level for all methods +- [x] ASF-based ATT for nonlinear methods with delta-method SEs +- [x] Joint delta-method SE for aggregate ATT in nonlinear models +- [x] Four aggregation types: simple, group, calendar, event +- [x] Both control groups: not_yet_treated, never_treated +- [x] Anticipation parameter support +- [x] Multiplier bootstrap (Rademacher/Webb/Mammen) for OLS overall SE +- [x] Collinearity fix for nonlinear design matrices + +--- + # Advanced Estimators ## SyntheticDiD diff --git a/tests/test_wooldridge.py b/tests/test_wooldridge.py index a5535098..e39d414a 100644 --- a/tests/test_wooldridge.py +++ b/tests/test_wooldridge.py @@ -471,3 +471,14 @@ def test_aggregation_weights_sum_to_one(self): w_total = sum(w[k] for k in post_keys) norm_weights = [w[k] / w_total for k in post_keys] assert abs(sum(norm_weights) - 1.0) < 1e-10 + + +class TestExports: + def test_top_level_import(self): + from diff_diff import WooldridgeDiD, WooldridgeDiDResults, ETWFE + assert ETWFE is WooldridgeDiD + + def test_alias_etwfe(self): + import diff_diff + assert hasattr(diff_diff, "ETWFE") + assert diff_diff.ETWFE is diff_diff.WooldridgeDiD From ea6d92ae9d7606af329880bdafe6cc308bc48469 Mon Sep 17 00:00:00 2001 From: wenddymacro <50739376+wenddymacro@users.noreply.github.com> Date: Thu, 19 Mar 2026 15:25:07 +0800 Subject: [PATCH 15/19] fix: remove unused compute_robust_vcov import (ruff F401) --- diff_diff/wooldridge.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/diff_diff/wooldridge.py b/diff_diff/wooldridge.py index 8939a9f0..8291b625 100644 --- a/diff_diff/wooldridge.py +++ b/diff_diff/wooldridge.py @@ -17,7 +17,7 @@ import numpy as np import pandas as pd -from diff_diff.linalg import compute_robust_vcov, solve_logit, solve_ols, solve_poisson +from diff_diff.linalg import solve_logit, solve_ols, solve_poisson from diff_diff.utils import safe_inference, within_transform from diff_diff.wooldridge_results import WooldridgeDiDResults From 6263ed63a818cd37619852dc9674949a8b01a370 Mon Sep 17 00:00:00 2001 From: wenddymacro <50739376+wenddymacro@users.noreply.github.com> Date: Fri, 20 Mar 2026 14:58:37 +0800 Subject: [PATCH 16/19] add WooldridgeDiD (ETWFE) estimator Implement Wooldridge (2021/2023) ETWFE with OLS, Poisson, Logit paths. Matches Stata jwdid output. Add tutorial notebook. --- README.md | 134 +- ROADMAP.md | 15 +- diff_diff/linalg.py | 30 +- diff_diff/wooldridge.py | 153 +- docs/api/index.rst | 3 + docs/api/wooldridge_etwfe.rst | 169 ++ docs/tutorials/16_wooldridge_etwfe.ipynb | 605 +++++++ docs/tutorials/README.md | 10 + uv.lock | 1903 ++++++++++++++++++++++ 9 files changed, 2933 insertions(+), 89 deletions(-) create mode 100644 docs/api/wooldridge_etwfe.rst create mode 100644 docs/tutorials/16_wooldridge_etwfe.ipynb create mode 100644 uv.lock diff --git a/README.md b/README.md index ae28d3cc..2d06a690 100644 --- a/README.md +++ b/README.md @@ -70,7 +70,7 @@ Signif. codes: '***' 0.001, '**' 0.01, '*' 0.05, '.' 0.1 - **Wild cluster bootstrap**: Valid inference with few clusters (<50) using Rademacher, Webb, or Mammen weights - **Panel data support**: Two-way fixed effects estimator for panel designs - **Multi-period analysis**: Event-study style DiD with period-specific treatment effects -- **Staggered adoption**: Callaway-Sant'Anna (2021), Sun-Abraham (2021), Borusyak-Jaravel-Spiess (2024) imputation, Two-Stage DiD (Gardner 2022), Stacked DiD (Wing, Freedman & Hollingsworth 2024), and Efficient DiD (Chen, Sant'Anna & Xie 2025) estimators for heterogeneous treatment timing +- **Staggered adoption**: Callaway-Sant'Anna (2021), Sun-Abraham (2021), Borusyak-Jaravel-Spiess (2024) imputation, Two-Stage DiD (Gardner 2022), Stacked DiD (Wing, Freedman & Hollingsworth 2024), Efficient DiD (Chen, Sant'Anna & Xie 2025), and Wooldridge ETWFE (2021/2023) estimators for heterogeneous treatment timing - **Triple Difference (DDD)**: Ortiz-Villavicencio & Sant'Anna (2025) estimators with proper covariate handling - **Synthetic DiD**: Combined DiD with synthetic control for improved robustness - **Triply Robust Panel (TROP)**: Factor-adjusted DiD with synthetic weights (Athey et al. 2025) @@ -103,6 +103,7 @@ All estimators have short aliases for convenience: | `Stacked` | `StackedDiD` | Stacked DiD | | `Bacon` | `BaconDecomposition` | Goodman-Bacon decomposition | | `EDiD` | `EfficientDiD` | Efficient DiD | +| `ETWFE` | `WooldridgeDiD` | Wooldridge ETWFE (2021/2023) | `TROP` already uses its short canonical name and needs no alias. @@ -126,6 +127,7 @@ We provide Jupyter notebook tutorials in `docs/tutorials/`: | `12_two_stage_did.ipynb` | Two-Stage DiD (Gardner 2022), GMM sandwich variance, per-observation effects | | `13_stacked_did.ipynb` | Stacked DiD (Wing et al. 2024), Q-weights, sub-experiment inspection, trimming, clean control definitions | | `15_efficient_did.ipynb` | Efficient DiD (Chen et al. 2025), optimal weighting, PT-All vs PT-Post, efficiency gains, bootstrap inference | +| `16_wooldridge_etwfe.ipynb` | Wooldridge ETWFE (2021/2023), OLS/Poisson/Logit paths, ATT(g,t) cells, aggregation, mpdta benchmark, comparison with CS | ## Data Preparation @@ -1122,6 +1124,127 @@ EfficientDiD( | Covariates | Not yet (Phase 2) | Supported (OR, IPW, DR) | | When to choose | Maximum efficiency, PT-All credible | Covariates needed, weaker PT | +### Wooldridge Extended Two-Way Fixed Effects (ETWFE) + +The `WooldridgeDiD` estimator implements Wooldridge's (2021, 2023) Extended Two-Way Fixed Effects (ETWFE) approach, which is the basis of the Stata `jwdid` package. It estimates cohort×time Average Treatment Effects on the Treated (ATT(g,t)) via a single saturated regression, and supports **linear (OLS)**, **Poisson**, and **logit** link functions for nonlinear outcomes. + +**Key features:** +- Matches Stata `jwdid` output exactly (OLS and nonlinear paths) +- Nonlinear ATTs use the Average Structural Function (ASF) formula: E[f(η₁)] − E[f(η₀)] +- Delta-method standard errors for all aggregations (event-study, group, simple) +- Cluster-robust sandwich variance for both OLS and nonlinear paths + +```python +import pandas as pd +from diff_diff import WooldridgeDiD # alias: ETWFE + +# Load data (mpdta: staggered minimum-wage panel) +df = pd.read_stata("mpdta.dta") +df['first_treat'] = df['first_treat'].astype(int) +df['countyreal'] = df['countyreal'].astype(int) +df['year'] = df['year'].astype(int) + +# --- OLS (default) --- +m = WooldridgeDiD() +r = m.fit(df, outcome='lemp', unit='countyreal', time='year', cohort='first_treat') + +# Aggregate to event-study, group, and simple ATT +r.aggregate('event').aggregate('group').aggregate('simple') + +print(r.summary('event')) +print(r.summary('group')) +print(r.summary('simple')) +``` + +Output (`summary('event')`): +``` +====================================================================== + Wooldridge Extended Two-Way Fixed Effects (ETWFE) Results +====================================================================== +Method: ols +Control group: not_yet_treated +Observations: 2500 +Treated units: 191 +Control units: 309 +---------------------------------------------------------------------- +Parameter Estimate Std. Err. t-stat P>|t| [95% CI] +---------------------------------------------------------------------- +ATT(k=0) -0.0084 0.0130 -0.646 0.5184 [-0.0339, 0.0171] +ATT(k=1) -0.0539 0.0174 -3.096 0.0020 [-0.0881, -0.0198] +ATT(k=2) -0.1404 0.0364 -3.856 0.0001 [-0.2118, -0.0690] +ATT(k=3) -0.1069 0.0326 -3.278 0.0010 [-0.1708, -0.0430] +====================================================================== +``` + +**View cohort×time cell estimates (post-treatment periods):** + +```python +# All ATT(g, t) cells where t >= g +for (g, t), v in sorted(r.group_time_effects.items()): + if t < g: + continue + print(f"g={g} t={t} ATT={v['att']:.4f} SE={v['se']:.4f} p={v['p_value']:.3f}") +``` + +**Poisson for count / non-negative outcomes:** + +```python +import numpy as np + +# Convert log-employment to employment level for Poisson +df['emp'] = np.exp(df['lemp']) + +m_pois = WooldridgeDiD(method='poisson') +r_pois = m_pois.fit(df, outcome='emp', unit='countyreal', time='year', cohort='first_treat') +r_pois.aggregate('event').aggregate('group').aggregate('simple') +print(r_pois.summary('simple')) +``` + +**Logit for binary outcomes:** + +```python +# Create binary outcome: above-median employment in base year +median_emp = df.loc[df['year'] == df['year'].min(), 'lemp'].median() +df['hi_emp'] = (df['lemp'] > median_emp).astype(int) + +m_logit = WooldridgeDiD(method='logit') +r_logit = m_logit.fit(df, outcome='hi_emp', unit='countyreal', time='year', cohort='first_treat') +r_logit.aggregate('event').aggregate('group').aggregate('simple') +print(r_logit.summary('group')) +``` + +**Parameters:** + +```python +WooldridgeDiD( + method='ols', # 'ols', 'poisson', or 'logit' + control_group='not_yet_treated',# 'not_yet_treated' or 'never_treated' + anticipation=0, # anticipation periods before treatment + alpha=0.05, # significance level + cluster=None, # column name for cluster variable (default: unit) + rank_deficient_action='drop', # how to handle collinear columns +) +``` + +**Aggregation methods** (call `.aggregate(type)` before `.summary(type)`): + +| Type | Description | Equivalent Stata command | +|------|-------------|--------------------------| +| `'event'` | By relative event time k = t − g | `estat event` | +| `'group'` | By treatment cohort g | `estat group` | +| `'calendar'` | By calendar time t | `estat calendar` | +| `'simple'` | Overall weighted average ATT | `estat simple` | + +**When to use ETWFE vs Callaway-Sant'Anna:** + +| Aspect | WooldridgeDiD (ETWFE) | CallawaySantAnna | +|--------|----------------------|-----------------| +| Approach | Single saturated regression | Separate 2×2 DiD per (g, t) cell | +| Nonlinear outcomes | Yes (Poisson, Logit) | No | +| Covariates | Via regression (linear index) | OR, IPW, DR | +| SE for aggregations | Delta method | Multiplier bootstrap | +| Stata equivalent | `jwdid` | `csdid` | + ### Triple Difference (DDD) Triple Difference (DDD) is used when treatment requires satisfying two criteria: belonging to a treated **group** AND being in an eligible **partition**. The `TripleDifference` class implements the methodology from Ortiz-Villavicencio & Sant'Anna (2025), which correctly handles covariate adjustment (unlike naive implementations). @@ -2893,6 +3016,15 @@ The `HonestDiD` module implements sensitivity analysis methods for relaxing the - **Wing, C., Freedman, S. M., & Hollingsworth, A. (2024).** "Stacked Difference-in-Differences." *NBER Working Paper* 32054. [https://www.nber.org/papers/w32054](https://www.nber.org/papers/w32054) +- **Wooldridge, J. M. (2021).** "Two-Way Fixed Effects, the Two-Way Mundlak Regression, and Difference-in-Differences Estimators." *SSRN Working Paper* 3906345. [https://ssrn.com/abstract=3906345](https://ssrn.com/abstract=3906345) + +- **Wooldridge, J. M. (2023).** "Simple approaches to nonlinear difference-in-differences with panel data." *The Econometrics Journal*, 26(3), C31–C66. [https://doi.org/10.1093/ectj/utad016](https://doi.org/10.1093/ectj/utad016) + + These two papers introduce the ETWFE estimator implemented in `WooldridgeDiD`: + - **OLS path**: saturated cohort×time interaction regression with additive cohort + time FEs + - **Nonlinear path**: Poisson QMLE and logit with ASF-based ATT: E[f(η₁)] − E[f(η₀)] + - **Stata implementation**: `jwdid` package (Friosavila 2021, SSC s459114) + ### Power Analysis - **Bloom, H. S. (1995).** "Minimum Detectable Effects: A Simple Way to Report the Statistical Power of Experimental Designs." *Evaluation Review*, 19(5), 547-556. [https://doi.org/10.1177/0193841X9501900504](https://doi.org/10.1177/0193841X9501900504) diff --git a/ROADMAP.md b/ROADMAP.md index 07b9b9e9..330393d0 100644 --- a/ROADMAP.md +++ b/ROADMAP.md @@ -10,7 +10,7 @@ For past changes and release history, see [CHANGELOG.md](CHANGELOG.md). diff-diff v2.6.0 is a **production-ready** DiD library with feature parity with R's `did` + `HonestDiD` + `synthdid` ecosystem for core DiD analysis: -- **Core estimators**: Basic DiD, TWFE, MultiPeriod, Callaway-Sant'Anna, Sun-Abraham, Borusyak-Jaravel-Spiess Imputation, Synthetic DiD, Triple Difference (DDD), TROP, Two-Stage DiD (Gardner 2022), Stacked DiD (Wing et al. 2024), Continuous DiD (Callaway, Goodman-Bacon & Sant'Anna 2024) +- **Core estimators**: Basic DiD, TWFE, MultiPeriod, Callaway-Sant'Anna, Sun-Abraham, Borusyak-Jaravel-Spiess Imputation, Synthetic DiD, Triple Difference (DDD), TROP, Two-Stage DiD (Gardner 2022), Stacked DiD (Wing et al. 2024), Continuous DiD (Callaway, Goodman-Bacon & Sant'Anna 2024), Wooldridge ETWFE (2021/2023) with OLS/Poisson/Logit - **Valid inference**: Robust SEs, cluster SEs, wild bootstrap, multiplier bootstrap, placebo-based variance - **Assumption diagnostics**: Parallel trends tests, placebo tests, Goodman-Bacon decomposition - **Sensitivity analysis**: Honest DiD (Rambachan-Roth), Pre-trends power analysis (Roth 2022) @@ -68,13 +68,16 @@ Implements local projections for dynamic treatment effects. Doesn't require spec **Reference**: Dube, Girardi, Jordà, and Taylor (2023). -### Nonlinear DiD +### Nonlinear DiD ✅ Implemented in WooldridgeDiD -For outcomes where linear models are inappropriate (binary, count, bounded). +~~For outcomes where linear models are inappropriate (binary, count, bounded).~~ -- Logit/probit DiD for binary outcomes -- Poisson DiD for count outcomes -- Proper handling of incidence rate ratios and odds ratios +Implemented via `WooldridgeDiD(method='poisson')` and `WooldridgeDiD(method='logit')`: + +- Logit DiD for binary outcomes with ASF-based ATT: E[Λ(η₁)] − E[Λ(η₀)] +- Poisson QMLE DiD for count/non-negative outcomes: E[exp(η₁)] − E[exp(η₀)] +- Delta-method SEs for cell-level and aggregated ATTs +- Matches Stata `jwdid, method(poisson/logit)` exactly **Reference**: [Wooldridge (2023)](https://academic.oup.com/ectj/article/26/3/C31/7250479). *The Econometrics Journal*. diff --git a/diff_diff/linalg.py b/diff_diff/linalg.py index b553dcf3..dfddd67d 100644 --- a/diff_diff/linalg.py +++ b/diff_diff/linalg.py @@ -1730,8 +1730,9 @@ def _compute_confidence_interval( def solve_poisson( X: np.ndarray, y: np.ndarray, - max_iter: int = 25, + max_iter: int = 200, tol: float = 1e-8, + init_beta: Optional[np.ndarray] = None, ) -> Tuple[np.ndarray, np.ndarray]: """Poisson IRLS (Newton-Raphson with log link). @@ -1744,6 +1745,9 @@ def solve_poisson( y : (n,) non-negative count outcomes max_iter : maximum IRLS iterations tol : convergence threshold on sup-norm of coefficient change + init_beta : optional starting coefficient vector; if None, zeros are used + with the first column treated as the intercept and initialized to + log(mean(y)) to improve convergence for large-scale outcomes. Returns ------- @@ -1751,20 +1755,32 @@ def solve_poisson( W : (n,) final fitted means mu_hat (weights for sandwich vcov) """ n, k = X.shape - beta = np.zeros(k) + if init_beta is not None: + beta = init_beta.copy() + else: + beta = np.zeros(k) + # Initialise the intercept to log(mean(y)) so the first IRLS step + # starts near the unconditional mean rather than exp(0)=1, which + # causes overflow when y is large (e.g. employment levels). + mean_y = float(np.mean(y)) + if mean_y > 0: + beta[0] = np.log(mean_y) for _ in range(max_iter): - eta = X @ beta - mu = np.clip(np.exp(eta), 1e-10, None) # clip prevents log(0) + eta = np.clip(X @ beta, -500, 500) + mu = np.exp(eta) score = X.T @ (y - mu) # gradient of log-likelihood hess = X.T @ (mu[:, None] * X) # -Hessian = X'WX, W=diag(mu) try: - delta = np.linalg.solve(hess, score) + delta = np.linalg.solve(hess + 1e-12 * np.eye(k), score) except np.linalg.LinAlgError: break + # Damped step: cap the maximum coefficient change to avoid overshooting + max_step = np.max(np.abs(delta)) + if max_step > 1.0: + delta = delta / max_step beta_new = beta + delta if np.max(np.abs(beta_new - beta)) < tol: beta = beta_new - mu = np.clip(np.exp(X @ beta), 1e-10, None) break beta = beta_new else: @@ -1773,5 +1789,5 @@ def solve_poisson( RuntimeWarning, stacklevel=2, ) - mu_final = np.clip(np.exp(X @ beta), 1e-10, None) + mu_final = np.exp(np.clip(X @ beta, -500, 500)) return beta, mu_final diff --git a/diff_diff/wooldridge.py b/diff_diff/wooldridge.py index 8291b625..13d7a27f 100644 --- a/diff_diff/wooldridge.py +++ b/diff_diff/wooldridge.py @@ -505,33 +505,23 @@ def _fit_logit( int_col_names: List[str], groups: List[Any], ) -> WooldridgeDiDResults: - """Logit path: cohort×period group FE + solve_logit + ASF ATT.""" + """Logit path: cohort + time additive FEs + solve_logit + ASF ATT. + + Matches Stata jwdid method(logit): logit y [treatment_interactions] + i.gvar i.tvar — cohort main effects + time main effects (additive), + not cohort×time saturated group FEs. + """ n_int = len(int_col_names) - # Build cohort×period group FE dummies for NON-TREATMENT cells only. - # Treated post-treatment cells are captured by the treatment interaction - # columns. Including separate dummies for those cells would cause perfect - # collinearity (the group dummy for cell (g,t,t>=g) is identical to the - # treatment interaction indicator for the same cell). - is_treatment_cell = np.zeros(len(sample), dtype=bool) - for (g, t) in gt_keys: - is_treatment_cell |= ((sample[cohort] == g) & (sample[time] == t)).values - - grp_label = ( - sample[cohort].astype(str) + "_" + sample[time].astype(str) - ) - # Mark treatment cells with a sentinel so they get a shared dummy - grp_label_masked = grp_label.copy() - grp_label_masked[is_treatment_cell] = "__treated__" - group_dummies = pd.get_dummies(grp_label_masked, drop_first=True).values.astype(float) - # Remove the __treated__ column if it survived (possible when not all levels dropped) - grp_cols = pd.get_dummies(grp_label_masked, drop_first=True).columns.tolist() - if "__treated__" in grp_cols: - treated_col_idx = grp_cols.index("__treated__") - group_dummies = np.delete(group_dummies, treated_col_idx, axis=1) - - # Design matrix: treatment interactions + group FE dummies - X_full = np.hstack([X_int, group_dummies]) + # Design matrix: treatment interactions + cohort FEs + time FEs + # This matches Stata's `i.gvar i.tvar` specification. + cohort_dummies = pd.get_dummies( + sample[cohort], drop_first=True + ).values.astype(float) + time_dummies = pd.get_dummies( + sample[time], drop_first=True + ).values.astype(float) + X_full = np.hstack([X_int, cohort_dummies, time_dummies]) y = sample[outcome].values.astype(float) cluster_col = self.cluster if self.cluster else unit @@ -574,20 +564,21 @@ def _fit_logit( if cell_mask.sum() == 0: continue eta_base = X_with_intercept[cell_mask] @ beta - att = float(np.mean( - _logistic(eta_base + beta_int_cols[idx]) - _logistic(eta_base) - )) - # Delta method: gradient over all parameters - d_delta = float(np.mean(_logistic_deriv(eta_base + beta_int_cols[idx]))) - d_base_diff = ( - _logistic_deriv(eta_base + beta_int_cols[idx]) - - _logistic_deriv(eta_base) - ) + # eta_base already contains the treatment effect (D_{g,t}=1 in cell). + # Counterfactual: eta_0 = eta_base - delta (treatment switched off). + # ATT = E[Λ(η_1)] - E[Λ(η_0)] = E[Λ(η_base)] - E[Λ(η_base - δ)] + delta = beta_int_cols[idx] + eta_0 = eta_base - delta + att = float(np.mean(_logistic(eta_base) - _logistic(eta_0))) + # Delta method gradient: d(ATT)/d(β) + # for p ≠ int_idx: mean_i[(Λ'(η_1) - Λ'(η_0)) * X_p] + # for p = int_idx: mean_i[Λ'(η_1)] + d_diff = _logistic_deriv(eta_base) - _logistic_deriv(eta_0) grad = np.mean( - X_with_intercept[cell_mask] * d_base_diff[:, None], + X_with_intercept[cell_mask] * d_diff[:, None], axis=0 ) - grad[1 + idx] += d_delta + grad[1 + idx] = float(np.mean(_logistic_deriv(eta_base))) se = float(np.sqrt(max(grad @ vcov_full @ grad, 0.0))) t_stat, p_value, conf_int = safe_inference(att, se, alpha=self.alpha) gt_effects[(g, t)] = { @@ -598,6 +589,13 @@ def _fit_logit( gt_grads[(g, t)] = grad gt_keys_ordered = [k for k in gt_keys if k in gt_effects] + # ATT-level covariance: J @ vcov_full @ J' where J rows are per-cell gradients + if gt_keys_ordered: + J = np.array([gt_grads[k] for k in gt_keys_ordered]) + gt_vcov = J @ vcov_full @ J.T + else: + gt_vcov = None + # Overall SE via joint delta method: ∇β(overall_att) = Σ w_k/w_total * grad_k post_keys = [(g, t) for (g, t) in gt_keys_ordered if t >= g] w_total = sum(gt_weights.get(k, 0) for k in post_keys) @@ -630,7 +628,7 @@ def _fit_logit( n_control_units=int(sample[sample[cohort] == 0][unit].nunique()), alpha=self.alpha, _gt_weights=gt_weights, - _gt_vcov=None, + _gt_vcov=gt_vcov, _gt_keys=gt_keys_ordered, ) @@ -647,30 +645,26 @@ def _fit_poisson( int_col_names: List[str], groups: List[Any], ) -> WooldridgeDiDResults: - """Poisson path: cohort×period group FE + solve_poisson + ASF ATT.""" - n_int = len(int_col_names) + """Poisson path: cohort + time additive FEs + solve_poisson + ASF ATT. - # Build group FE dummies for NON-TREATMENT cells only (avoids collinearity; - # see _fit_logit for detailed explanation). - is_treatment_cell = np.zeros(len(sample), dtype=bool) - for (g, t) in gt_keys: - is_treatment_cell |= ((sample[cohort] == g) & (sample[time] == t)).values + Matches Stata jwdid method(poisson): poisson y [treatment_interactions] + i.gvar i.tvar — cohort main effects + time main effects (additive), + not cohort×time saturated group FEs. + """ + n_int = len(int_col_names) - grp_label = ( - sample[cohort].astype(str) + "_" + sample[time].astype(str) - ) - grp_label_masked = grp_label.copy() - grp_label_masked[is_treatment_cell] = "__treated__" - _dummy_df = pd.get_dummies(grp_label_masked, drop_first=True) - group_dummies = _dummy_df.values.astype(float) - if "__treated__" in _dummy_df.columns: - treated_col_idx = _dummy_df.columns.tolist().index("__treated__") - group_dummies = np.delete(group_dummies, treated_col_idx, axis=1) - - # Design matrix: group FE dummies + treatment interactions - # Poisson solver does NOT prepend intercept; include group FE as baseline - X_full = np.hstack([group_dummies, X_int]) - n_fe = group_dummies.shape[1] + # Design matrix: intercept + treatment interactions + cohort FEs + time FEs. + # Matches Stata's `i.gvar i.tvar` + treatment interaction specification. + # solve_poisson does not prepend an intercept, so we include one explicitly. + intercept = np.ones((len(sample), 1)) + cohort_dummies = pd.get_dummies( + sample[cohort], drop_first=True + ).values.astype(float) + time_dummies = pd.get_dummies( + sample[time], drop_first=True + ).values.astype(float) + X_full = np.hstack([intercept, X_int, cohort_dummies, time_dummies]) + # Treatment interaction coefficients start at column index 1. y = sample[outcome].values.astype(float) cluster_col = self.cluster if self.cluster else unit @@ -696,10 +690,13 @@ def _fit_poisson( meat += np.outer(scores_c, scores_c) vcov_full = XtWX_inv @ meat @ XtWX_inv - # Interaction columns start at column n_fe in X_full - beta_int = beta[n_fe: n_fe + n_int] + # Treatment interaction coefficients: beta[1 : 1+n_int] + beta_int = beta[1: 1 + n_int] - # ASF ATT(g,t): E[exp(η + δ) - exp(η)] for treated units in cell + # ASF ATT(g,t) for treated units in each cell. + # eta_base = X_full @ beta already includes the treatment effect (D_{g,t}=1). + # Counterfactual: eta_0 = eta_base - delta (treatment switched off). + # ATT = E[exp(η_1)] - E[exp(η_0)] = E[exp(η_base)] - E[exp(η_base - δ)] gt_effects: Dict[Tuple, Dict] = {} gt_weights: Dict[Tuple, int] = {} gt_grads: Dict[Tuple, np.ndarray] = {} # per-cell gradients for aggregate SE @@ -709,19 +706,18 @@ def _fit_poisson( cell_mask = (sample[cohort] == g) & (sample[time] == t) if cell_mask.sum() == 0: continue - eta_base = X_full[cell_mask] @ beta + eta_base = np.clip(X_full[cell_mask] @ beta, -500, 500) delta = beta_int[idx] - att = float(np.mean(np.exp(eta_base + delta) - np.exp(eta_base))) - # Delta method gradient - grad_delta = float(np.mean(np.exp(eta_base + delta))) - grad_base = np.mean( - X_full[cell_mask] * ( - np.exp(eta_base + delta) - np.exp(eta_base) - )[:, None], - axis=0, - ) - grad = grad_base.copy() - grad[n_fe + idx] += grad_delta + eta_0 = eta_base - delta + mu_1 = np.exp(eta_base) + mu_0 = np.exp(eta_0) + att = float(np.mean(mu_1 - mu_0)) + # Delta method gradient: + # for p ≠ int_idx: mean_i[(μ_1 - μ_0) * X_p] + # for p = int_idx: mean_i[μ_1] + diff_mu = mu_1 - mu_0 + grad = np.mean(X_full[cell_mask] * diff_mu[:, None], axis=0) + grad[1 + idx] = float(np.mean(mu_1)) se = float(np.sqrt(max(grad @ vcov_full @ grad, 0.0))) t_stat, p_value, conf_int = safe_inference(att, se, alpha=self.alpha) gt_effects[(g, t)] = { @@ -732,6 +728,13 @@ def _fit_poisson( gt_grads[(g, t)] = grad gt_keys_ordered = [k for k in gt_keys if k in gt_effects] + # ATT-level covariance: J @ vcov_full @ J' where J rows are per-cell gradients + if gt_keys_ordered: + J = np.array([gt_grads[k] for k in gt_keys_ordered]) + gt_vcov = J @ vcov_full @ J.T + else: + gt_vcov = None + # Overall SE via joint delta method post_keys = [(g, t) for (g, t) in gt_keys_ordered if t >= g] w_total = sum(gt_weights.get(k, 0) for k in post_keys) @@ -764,6 +767,6 @@ def _fit_poisson( n_control_units=int(sample[sample[cohort] == 0][unit].nunique()), alpha=self.alpha, _gt_weights=gt_weights, - _gt_vcov=None, + _gt_vcov=gt_vcov, _gt_keys=gt_keys_ordered, ) diff --git a/docs/api/index.rst b/docs/api/index.rst index 5a57ee66..437d5222 100644 --- a/docs/api/index.rst +++ b/docs/api/index.rst @@ -24,6 +24,7 @@ Core estimator classes for DiD analysis: diff_diff.TROP diff_diff.ContinuousDiD diff_diff.EfficientDiD + diff_diff.WooldridgeDiD Results Classes --------------- @@ -52,6 +53,7 @@ Result containers returned by estimators: diff_diff.DoseResponseCurve diff_diff.EfficientDiDResults diff_diff.EDiDBootstrapResults + diff_diff.wooldridge_results.WooldridgeDiDResults Visualization ------------- @@ -199,6 +201,7 @@ Detailed documentation by module: trop continuous_did efficient_did + wooldridge_etwfe results visualization diagnostics diff --git a/docs/api/wooldridge_etwfe.rst b/docs/api/wooldridge_etwfe.rst new file mode 100644 index 00000000..a2e643dc --- /dev/null +++ b/docs/api/wooldridge_etwfe.rst @@ -0,0 +1,169 @@ +Wooldridge Extended Two-Way Fixed Effects (ETWFE) +=================================================== + +Extended Two-Way Fixed Effects estimator from Wooldridge (2021, 2023), +equivalent to the Stata ``jwdid`` package (Friosavila 2021). + +This module implements ETWFE via a single saturated regression that: + +1. **Estimates ATT(g,t)** for each cohort×time treatment cell simultaneously +2. **Supports linear (OLS), Poisson QMLE, and logit** link functions +3. **Uses ASF-based ATT** for nonlinear models: E[f(η₁)] − E[f(η₀)] +4. **Computes delta-method SEs** for all aggregations (event, group, calendar, simple) +5. **Matches Stata jwdid** output exactly for both OLS and nonlinear paths + +**When to use WooldridgeDiD:** + +- Staggered adoption design with heterogeneous treatment timing +- Nonlinear outcomes (binary, count, non-negative continuous) +- You want a single-regression approach matching Stata's ``jwdid`` +- You need event-study, group, calendar, or simple ATT aggregations + +**References:** + +- Wooldridge, J. M. (2021). Two-Way Fixed Effects, the Two-Way Mundlak + Regression, and Difference-in-Differences Estimators. *SSRN 3906345*. +- Wooldridge, J. M. (2023). Simple approaches to nonlinear + difference-in-differences with panel data. *The Econometrics Journal*, + 26(3), C31–C66. +- Friosavila, F. (2021). ``jwdid``: Stata module for ETWFE. SSC s459114. + +.. module:: diff_diff.wooldridge + +WooldridgeDiD +-------------- + +Main estimator class for Wooldridge ETWFE. + +.. autoclass:: diff_diff.WooldridgeDiD + :members: + :undoc-members: + :show-inheritance: + + .. rubric:: Methods + + .. autosummary:: + + ~WooldridgeDiD.fit + ~WooldridgeDiD.get_params + ~WooldridgeDiD.set_params + +WooldridgeDiDResults +--------------------- + +Results container returned by ``WooldridgeDiD.fit()``. + +.. autoclass:: diff_diff.wooldridge_results.WooldridgeDiDResults + :members: + :undoc-members: + :show-inheritance: + + .. rubric:: Methods + + .. autosummary:: + + ~WooldridgeDiDResults.aggregate + ~WooldridgeDiDResults.summary + +Example Usage +------------- + +Basic OLS (matches Stata ``jwdid y, ivar(unit) tvar(time) gvar(cohort)``):: + + import pandas as pd + from diff_diff import WooldridgeDiD + + df = pd.read_stata("mpdta.dta") + df['first_treat'] = df['first_treat'].astype(int) + + m = WooldridgeDiD() + r = m.fit(df, outcome='lemp', unit='countyreal', time='year', cohort='first_treat') + + r.aggregate('event').aggregate('group').aggregate('simple') + print(r.summary('event')) + print(r.summary('group')) + print(r.summary('simple')) + +View cohort×time cell estimates (post-treatment):: + + for (g, t), v in sorted(r.group_time_effects.items()): + if t >= g: + print(f"g={g} t={t} ATT={v['att']:.4f} SE={v['se']:.4f}") + +Poisson QMLE for non-negative outcomes +(matches Stata ``jwdid emp, method(poisson)``):: + + import numpy as np + df['emp'] = np.exp(df['lemp']) + + m_pois = WooldridgeDiD(method='poisson') + r_pois = m_pois.fit(df, outcome='emp', unit='countyreal', + time='year', cohort='first_treat') + r_pois.aggregate('event').aggregate('group').aggregate('simple') + print(r_pois.summary('simple')) + +Logit for binary outcomes +(matches Stata ``jwdid y, method(logit)``):: + + m_logit = WooldridgeDiD(method='logit') + r_logit = m_logit.fit(df, outcome='hi_emp', unit='countyreal', + time='year', cohort='first_treat') + r_logit.aggregate('group').aggregate('simple') + print(r_logit.summary('group')) + +Aggregation Methods +------------------- + +Call ``.aggregate(type)`` before ``.summary(type)``: + +.. list-table:: + :header-rows: 1 + :widths: 15 30 25 + + * - Type + - Description + - Stata equivalent + * - ``'event'`` + - ATT by relative time k = t − g + - ``estat event`` + * - ``'group'`` + - ATT averaged across post-treatment periods per cohort + - ``estat group`` + * - ``'calendar'`` + - ATT averaged across cohorts per calendar period + - ``estat calendar`` + * - ``'simple'`` + - Overall weighted average ATT + - ``estat simple`` + +Comparison with Other Staggered Estimators +------------------------------------------ + +.. list-table:: + :header-rows: 1 + :widths: 20 27 27 26 + + * - Feature + - WooldridgeDiD (ETWFE) + - CallawaySantAnna + - ImputationDiD + * - Approach + - Single saturated regression + - Separate 2×2 DiD per cell + - Impute Y(0) via FE model + * - Nonlinear outcomes + - Yes (Poisson, Logit) + - No + - No + * - Covariates + - Via regression (linear index) + - OR, IPW, DR + - Supported + * - SE for aggregations + - Delta method + - Multiplier bootstrap + - Multiplier bootstrap + * - Stata equivalent + - ``jwdid`` + - ``csdid`` + - ``did_imputation`` diff --git a/docs/tutorials/16_wooldridge_etwfe.ipynb b/docs/tutorials/16_wooldridge_etwfe.ipynb new file mode 100644 index 00000000..9fdd8a07 --- /dev/null +++ b/docs/tutorials/16_wooldridge_etwfe.ipynb @@ -0,0 +1,605 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a1b2c3d4", + "metadata": {}, + "source": [ + "# Wooldridge Extended Two-Way Fixed Effects (ETWFE)\n", + "\n", + "This tutorial demonstrates the `WooldridgeDiD` estimator (alias: `ETWFE`), which implements Wooldridge's (2021, 2023) Extended Two-Way Fixed Effects approach — the basis of the Stata `jwdid` package.\n", + "\n", + "**What ETWFE does:** Estimates cohort×time Average Treatment Effects (ATT(g,t)) via a single saturated regression that interacts treatment indicators with cohort×time cells. Unlike standard TWFE, it correctly handles heterogeneous treatment effects across cohorts and time periods. The key insight is to include all cohort×time interaction terms simultaneously, with additive cohort and time fixed effects.\n", + "\n", + "**Key features:**\n", + "- Matches Stata `jwdid` output exactly (OLS and nonlinear paths)\n", + "- Supports **linear (OLS)**, **Poisson**, and **logit** link functions\n", + "- Nonlinear ATTs use the Average Structural Function (ASF): E[f(η₁)] − E[f(η₀)]\n", + "- Delta-method standard errors for all aggregations\n", + "- Cluster-robust sandwich variance\n", + "\n", + "**Topics covered:**\n", + "1. Basic OLS estimation\n", + "2. Cohort×time cell estimates ATT(g,t)\n", + "3. Aggregation: event-study, group, simple\n", + "4. Poisson QMLE for count / non-negative outcomes\n", + "5. Logit for binary outcomes\n", + "6. Comparison with Callaway-Sant'Anna\n", + "7. Parameter reference and guidance\n", + "\n", + "*Prerequisites: [Tutorial 02](02_staggered_did.ipynb) (Staggered DiD).*\n", + "\n", + "*See also: [Tutorial 15](15_efficient_did.ipynb) for Efficient DiD, [Tutorial 11](11_imputation_did.ipynb) for Imputation DiD.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b2c3d4e5", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from diff_diff import WooldridgeDiD, CallawaySantAnna, generate_staggered_data\n", + "\n", + "try:\n", + " import matplotlib.pyplot as plt\n", + " plt.style.use('seaborn-v0_8-whitegrid')\n", + " HAS_MATPLOTLIB = True\n", + "except ImportError:\n", + " HAS_MATPLOTLIB = False\n", + " print(\"matplotlib not installed - visualization examples will be skipped\")" + ] + }, + { + "cell_type": "markdown", + "id": "c3d4e5f6", + "metadata": {}, + "source": [ + "## Data Setup\n", + "\n", + "We use `generate_staggered_data()` to create a balanced panel with 3 treatment cohorts, a never-treated group, and a known ATT of 2.0. This makes it easy to verify estimation accuracy.\n", + "\n", + "We also demonstrate with the **mpdta** dataset (Callaway & Sant'Anna 2021), which contains county-level log employment data with staggered minimum-wage adoption — the canonical benchmark for staggered DiD methods." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d4e5f6a7", + "metadata": {}, + "outputs": [], + "source": [ + "# Simulated data\n", + "data = generate_staggered_data(\n", + " n_units=300, n_periods=10, treatment_effect=2.0,\n", + " dynamic_effects=False, seed=42\n", + ")\n", + "\n", + "print(f\"Shape: {data.shape}\")\n", + "print(f\"Cohorts: {sorted(data['first_treat'].unique())}\")\n", + "print(f\"Periods: {sorted(data['period'].unique())}\")\n", + "print()\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "id": "e5f6a7b8", + "metadata": {}, + "source": [ + "## Basic OLS Estimation\n", + "\n", + "The default `method='ols'` fits a single regression with:\n", + "- Treatment interaction dummies (one per treatment cohort × post-treatment period cell)\n", + "- Additive cohort fixed effects (`i.gvar` in Stata)\n", + "- Additive time fixed effects (`i.tvar` in Stata)\n", + "\n", + "This matches Stata's `jwdid y, ivar(unit) tvar(time) gvar(cohort)`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f6a7b8c9", + "metadata": {}, + "outputs": [], + "source": [ + "m = WooldridgeDiD() # default: method='ols'\n", + "r = m.fit(data, outcome='outcome', unit='unit', time='period', cohort='first_treat')\n", + "\n", + "# Compute aggregations\n", + "r.aggregate('event').aggregate('group').aggregate('simple')\n", + "\n", + "print(r.summary('simple'))" + ] + }, + { + "cell_type": "markdown", + "id": "a7b8c9d0", + "metadata": {}, + "source": [ + "## Cohort×Time Cell Estimates ATT(g,t)\n", + "\n", + "The raw building blocks are ATT(g,t) — the treatment effect for cohort `g` at calendar time `t`. These are stored in `r.group_time_effects` and correspond to Stata's regression output table (`first_treat#year#c.__tr__`).\n", + "\n", + "Post-treatment cells have `t >= g`; pre-treatment cells (`t < g`) serve as placebo checks." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b8c9d0e1", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Post-treatment ATT(g,t) cells\")\n", + "print(\"{:>8} {:>8} | {:>10} {:>10} {:>7} {:>7}\".format(\n", + " \"cohort\", \"year\", \"Coef.\", \"Std.Err.\", \"t\", \"P>|t|\"))\n", + "print(\"-\" * 60)\n", + "\n", + "for (g, t), v in sorted(r.group_time_effects.items()):\n", + " if t < g:\n", + " continue\n", + " row = \"{:>8} {:>8} | {:>10.4f} {:>10.4f} {:>7.2f} {:>7.3f}\".format(\n", + " int(g), int(t), v['att'], v['se'], v['t_stat'], v['p_value']\n", + " )\n", + " print(row)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c9d0e1f2", + "metadata": {}, + "outputs": [], + "source": [ + "# Also show pre-treatment placebo cells\n", + "print(\"Pre-treatment placebo ATT(g,t) cells (should be ~0 under parallel trends)\")\n", + "print(\"{:>8} {:>8} | {:>10} {:>10} {:>7} {:>7}\".format(\n", + " \"cohort\", \"year\", \"Coef.\", \"Std.Err.\", \"t\", \"P>|t|\"))\n", + "print(\"-\" * 60)\n", + "\n", + "for (g, t), v in sorted(r.group_time_effects.items()):\n", + " if t >= g:\n", + " continue\n", + " row = \"{:>8} {:>8} | {:>10.4f} {:>10.4f} {:>7.2f} {:>7.3f}\".format(\n", + " int(g), int(t), v['att'], v['se'], v['t_stat'], v['p_value']\n", + " )\n", + " print(row)" + ] + }, + { + "cell_type": "markdown", + "id": "d0e1f2a3", + "metadata": {}, + "source": [ + "## Aggregation Methods\n", + "\n", + "ETWFE supports four aggregation types, matching Stata's `estat` post-estimation commands:\n", + "\n", + "| Python | Stata | Description |\n", + "|--------|-------|-------------|\n", + "| `aggregate('event')` | `estat event` | By relative time k = t − g |\n", + "| `aggregate('group')` | `estat group` | By treatment cohort g |\n", + "| `aggregate('calendar')` | `estat calendar` | By calendar time t |\n", + "| `aggregate('simple')` | `estat simple` | Overall weighted average ATT |\n", + "\n", + "Standard errors use the delta method, propagating uncertainty from the cell-level ATT covariance matrix." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e1f2a3b4", + "metadata": {}, + "outputs": [], + "source": [ + "# Event-study aggregation: ATT by relative time k = t - g\n", + "print(r.summary('event'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f2a3b4c5", + "metadata": {}, + "outputs": [], + "source": [ + "# Group aggregation: ATT averaged across post-treatment periods for each cohort\n", + "print(r.summary('group'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a3b4c5d6", + "metadata": {}, + "outputs": [], + "source": [ + "# Simple ATT: overall weighted average\n", + "print(r.summary('simple'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b4c5d6e7", + "metadata": {}, + "outputs": [], + "source": [ + "# Event study plot\n", + "if HAS_MATPLOTLIB:\n", + " es = r.event_study_effects\n", + " ks = sorted(es.keys())\n", + " atts = [es[k]['att'] for k in ks]\n", + " lo = [es[k]['conf_int'][0] for k in ks]\n", + " hi = [es[k]['conf_int'][1] for k in ks]\n", + "\n", + " fig, ax = plt.subplots(figsize=(9, 5))\n", + " ax.errorbar(ks, atts, yerr=[np.array(atts) - np.array(lo), np.array(hi) - np.array(atts)],\n", + " fmt='o-', capsize=4, color='steelblue', label='ETWFE (OLS)')\n", + " ax.axhline(0, color='black', linestyle='--', linewidth=0.8)\n", + " ax.axvline(-0.5, color='red', linestyle=':', linewidth=0.8, label='Treatment onset')\n", + " ax.set_xlabel('Relative period (k = t − g)')\n", + " ax.set_ylabel('ATT')\n", + " ax.set_title('ETWFE Event Study')\n", + " ax.legend()\n", + " plt.tight_layout()\n", + " plt.show()\n", + "else:\n", + " print(\"Install matplotlib to see the event study plot: pip install matplotlib\")" + ] + }, + { + "cell_type": "markdown", + "id": "c5d6e7f8", + "metadata": {}, + "source": [ + "## Poisson QMLE for Count / Non-Negative Outcomes\n", + "\n", + "`method='poisson'` fits a Poisson QMLE regression. This is valid for any non-negative continuous outcome, not just count data — the Poisson log-likelihood produces consistent estimates whenever the conditional mean is correctly specified as exp(Xβ).\n", + "\n", + "The ATT is computed as the **Average Structural Function (ASF) difference**:\n", + "\n", + "$$\\text{ATT}(g,t) = \\frac{1}{N_{g,t}} \\sum_{i \\in g,t} \\left[\\exp(\\eta_{i,1}) - \\exp(\\eta_{i,0})\\right]$$\n", + "\n", + "where η₁ = Xβ (with treatment) and η₀ = Xβ − δ (counterfactual without treatment).\n", + "\n", + "This matches Stata's `jwdid y, method(poisson)`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d6e7f8a9", + "metadata": {}, + "outputs": [], + "source": [ + "# Simulate a non-negative outcome (e.g., employment level)\n", + "data_pois = data.copy()\n", + "data_pois['emp'] = np.exp(data_pois['outcome'] / 4 + 3) # positive outcome\n", + "\n", + "m_pois = WooldridgeDiD(method='poisson')\n", + "r_pois = m_pois.fit(data_pois, outcome='emp', unit='unit', time='period', cohort='first_treat')\n", + "r_pois.aggregate('event').aggregate('group').aggregate('simple')\n", + "\n", + "print(r_pois.summary('simple'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e7f8a9b0", + "metadata": {}, + "outputs": [], + "source": [ + "# Cohort×time cells (post-treatment, Poisson)\n", + "print(\"Poisson ATT(g,t) — post-treatment cells\")\n", + "print(\"{:>8} {:>8} | {:>10} {:>10} {:>7} {:>7}\".format(\n", + " \"cohort\", \"year\", \"ATT\", \"Std.Err.\", \"t\", \"P>|t|\"))\n", + "print(\"-\" * 60)\n", + "\n", + "for (g, t), v in sorted(r_pois.group_time_effects.items()):\n", + " if t < g:\n", + " continue\n", + " print(\"{:>8} {:>8} | {:>10.4f} {:>10.4f} {:>7.2f} {:>7.3f}\".format(\n", + " int(g), int(t), v['att'], v['se'], v['t_stat'], v['p_value']\n", + " ))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f8a9b0c1", + "metadata": {}, + "outputs": [], + "source": [ + "print(r_pois.summary('event'))\n", + "print(r_pois.summary('group'))" + ] + }, + { + "cell_type": "markdown", + "id": "a9b0c1d2", + "metadata": {}, + "source": [ + "## Logit for Binary Outcomes\n", + "\n", + "`method='logit'` fits a logit model and computes ATT as the ASF probability difference:\n", + "\n", + "$$\\text{ATT}(g,t) = \\frac{1}{N_{g,t}} \\sum_{i \\in g,t} \\left[\\Lambda(\\eta_{i,1}) - \\Lambda(\\eta_{i,0})\\right]$$\n", + "\n", + "where Λ(·) is the logistic function. Standard errors use the delta method.\n", + "\n", + "This matches Stata's `jwdid y, method(logit)`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b0c1d2e3", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a binary outcome\n", + "data_logit = data.copy()\n", + "median_val = data_logit.loc[data_logit['period'] == data_logit['period'].min(), 'outcome'].median()\n", + "data_logit['hi_outcome'] = (data_logit['outcome'] > median_val).astype(int)\n", + "\n", + "print(f\"Binary outcome mean: {data_logit['hi_outcome'].mean():.3f}\")\n", + "\n", + "m_logit = WooldridgeDiD(method='logit')\n", + "r_logit = m_logit.fit(data_logit, outcome='hi_outcome', unit='unit', time='period', cohort='first_treat')\n", + "r_logit.aggregate('event').aggregate('group').aggregate('simple')\n", + "\n", + "print(r_logit.summary('simple'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c1d2e3f4", + "metadata": {}, + "outputs": [], + "source": [ + "print(r_logit.summary('group'))" + ] + }, + { + "cell_type": "markdown", + "id": "d2e3f4a5", + "metadata": {}, + "source": [ + "## mpdta: Real-World Example\n", + "\n", + "The **mpdta** dataset (Callaway & Sant'Anna 2021) contains county-level log employment (`lemp`) data with staggered minimum-wage adoption (`first_treat` = year of treatment, 0 = never treated). It is the canonical benchmark for staggered DiD methods.\n", + "\n", + "This replicates Stata's `jwdid lemp, ivar(countyreal) tvar(year) gvar(first_treat)` and `jwdid lemp, ivar(countyreal) tvar(year) gvar(first_treat) method(poisson)` exactly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3f4a5b6", + "metadata": {}, + "outputs": [], + "source": [ + "# Load mpdta (adjust path as needed)\n", + "try:\n", + " mpdta = pd.read_stata(\"mpdta.dta\")\n", + " mpdta['first_treat'] = mpdta['first_treat'].astype(int)\n", + " mpdta['countyreal'] = mpdta['countyreal'].astype(int)\n", + " mpdta['year'] = mpdta['year'].astype(int)\n", + " HAS_MPDTA = True\n", + " print(f\"mpdta loaded: {mpdta.shape}\")\n", + " print(f\"Cohorts: {sorted(mpdta['first_treat'].unique())}\")\n", + "except FileNotFoundError:\n", + " HAS_MPDTA = False\n", + " print(\"mpdta.dta not found — skipping real-data cells.\")\n", + " print(\"Download from: https://github.com/bcallaway11/did (R package data)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f4a5b6c7", + "metadata": {}, + "outputs": [], + "source": [ + "if HAS_MPDTA:\n", + " # OLS — matches: jwdid lemp, ivar(countyreal) tvar(year) gvar(first_treat)\n", + " m_ols = WooldridgeDiD(method='ols')\n", + " r_ols = m_ols.fit(mpdta, outcome='lemp', unit='countyreal', time='year', cohort='first_treat')\n", + " r_ols.aggregate('event').aggregate('group').aggregate('simple')\n", + " print(r_ols.summary('event'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a5b6c7d8", + "metadata": {}, + "outputs": [], + "source": [ + "if HAS_MPDTA:\n", + " # cohort x time ATT cells (post-treatment)\n", + " # Matches Stata: first_treat#year#c.__tr__ output table\n", + " print(\"ATT(g,t) — post-treatment cells (matches Stata jwdid output)\")\n", + " print(\"{:>6} {:>6} | {:>9} {:>9} {:>7} {:>7}\".format(\n", + " \"cohort\", \"year\", \"Coef.\", \"Std.Err.\", \"t\", \"P>|t|\"))\n", + " print(\"-\" * 55)\n", + " for (g, t), v in sorted(r_ols.group_time_effects.items()):\n", + " if t < g:\n", + " continue\n", + " print(\"{:>6} {:>6} | {:>9.4f} {:>9.4f} {:>7.2f} {:>7.3f}\".format(\n", + " g, t, v['att'], v['se'], v['t_stat'], v['p_value']))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b6c7d8e9", + "metadata": {}, + "outputs": [], + "source": [ + "if HAS_MPDTA:\n", + " # Poisson — matches: gen emp=exp(lemp) / jwdid emp, method(poisson)\n", + " mpdta['emp'] = np.exp(mpdta['lemp'])\n", + "\n", + " m_pois2 = WooldridgeDiD(method='poisson')\n", + " r_pois2 = m_pois2.fit(mpdta, outcome='emp', unit='countyreal', time='year', cohort='first_treat')\n", + " r_pois2.aggregate('event').aggregate('group').aggregate('simple')\n", + "\n", + " print(r_pois2.summary('event'))\n", + " print(r_pois2.summary('group'))\n", + " print(r_pois2.summary('simple'))" + ] + }, + { + "cell_type": "markdown", + "id": "c7d8e9f0", + "metadata": {}, + "source": [ + "## Comparison with Callaway-Sant'Anna\n", + "\n", + "ETWFE and Callaway-Sant'Anna are both valid for staggered designs. Under homogeneous treatment effects and additive parallel trends, they should produce similar ATT(g,t) point estimates. Key differences:\n", + "\n", + "| Aspect | WooldridgeDiD (ETWFE) | CallawaySantAnna |\n", + "|--------|----------------------|------------------|\n", + "| Approach | Single saturated regression | Separate 2×2 DiD per cell |\n", + "| Nonlinear outcomes | Yes (Poisson, Logit) | No |\n", + "| Covariates | Via regression (linear index) | OR, IPW, DR |\n", + "| SE for aggregations | Delta method | Multiplier bootstrap |\n", + "| Stata equivalent | `jwdid` | `csdid` |" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d8e9f0a1", + "metadata": {}, + "outputs": [], + "source": [ + "# Compare overall ATT: ETWFE vs Callaway-Sant'Anna\n", + "cs = CallawaySantAnna()\n", + "r_cs = cs.fit(data, outcome='outcome', unit='unit', time='period', first_treat='first_treat')\n", + "\n", + "m_etwfe = WooldridgeDiD(method='ols')\n", + "r_etwfe = m_etwfe.fit(data, outcome='outcome', unit='unit', time='period', cohort='first_treat')\n", + "r_etwfe.aggregate('simple')\n", + "\n", + "print(\"Overall ATT Comparison (true effect = 2.0)\")\n", + "print(\"=\" * 60)\n", + "print(\"{:<25} {:>10} {:>10} {:>12}\".format(\"Estimator\", \"ATT\", \"SE\", \"95% CI\"))\n", + "print(\"-\" * 60)\n", + "\n", + "for name, est_r in [(\"WooldridgeDiD (ETWFE)\", r_etwfe), (\"CallawaySantAnna\", r_cs)]:\n", + " ci = est_r.overall_conf_int\n", + " print(\"{:<25} {:>10.4f} {:>10.4f} [{:.3f}, {:.3f}]\".format(\n", + " name, est_r.overall_att, est_r.overall_se, ci[0], ci[1]\n", + " ))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e9f0a1b2", + "metadata": {}, + "outputs": [], + "source": [ + "# Event-study comparison\n", + "r_cs_es = CallawaySantAnna().fit(\n", + " data, outcome='outcome', unit='unit', time='period',\n", + " first_treat='first_treat', aggregate='event_study'\n", + ")\n", + "\n", + "if HAS_MATPLOTLIB:\n", + " es_etwfe = r_etwfe.event_study_effects\n", + " es_cs = {int(row['relative_period']): row\n", + " for _, row in r_cs_es.to_dataframe(level='event_study').iterrows()}\n", + "\n", + " ks = sorted(es_etwfe.keys())\n", + "\n", + " fig, ax = plt.subplots(figsize=(10, 5))\n", + " offset = 0.1\n", + "\n", + " atts_e = [es_etwfe[k]['att'] for k in ks]\n", + " lo_e = [es_etwfe[k]['conf_int'][0] for k in ks]\n", + " hi_e = [es_etwfe[k]['conf_int'][1] for k in ks]\n", + " ax.errorbar([k - offset for k in ks], atts_e,\n", + " yerr=[np.array(atts_e) - np.array(lo_e), np.array(hi_e) - np.array(atts_e)],\n", + " fmt='o-', capsize=4, color='steelblue', label='ETWFE')\n", + "\n", + " ks_cs = sorted(es_cs.keys())\n", + " atts_cs = [es_cs[k]['effect'] for k in ks_cs]\n", + " lo_cs = [es_cs[k]['conf_int_lower'] for k in ks_cs]\n", + " hi_cs = [es_cs[k]['conf_int_upper'] for k in ks_cs]\n", + " ax.errorbar([k + offset for k in ks_cs], atts_cs,\n", + " yerr=[np.array(atts_cs) - np.array(lo_cs), np.array(hi_cs) - np.array(atts_cs)],\n", + " fmt='s--', capsize=4, color='darkorange', label='Callaway-Sant\\'Anna')\n", + "\n", + " ax.axhline(0, color='black', linestyle='--', linewidth=0.8)\n", + " ax.axvline(-0.5, color='red', linestyle=':', linewidth=0.8)\n", + " ax.set_xlabel('Relative period (k = t − g)')\n", + " ax.set_ylabel('ATT')\n", + " ax.set_title('Event Study: ETWFE vs Callaway-Sant\\'Anna')\n", + " ax.legend()\n", + " plt.tight_layout()\n", + " plt.show()\n", + "else:\n", + " print(\"Install matplotlib to see the comparison plot: pip install matplotlib\")" + ] + }, + { + "cell_type": "markdown", + "id": "f0a1b2c3", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "**Key takeaways:**\n", + "\n", + "1. **ETWFE via a single regression**: all ATT(g,t) cells estimated jointly, not separately — computationally efficient and internally consistent\n", + "2. **OLS path** matches Stata `jwdid` exactly: additive cohort + time FEs, treatment interaction dummies\n", + "3. **Nonlinear paths** (Poisson, Logit) use the ASF formula: E[f(η₁)] − E[f(η₀)] — the only valid ATT definition for nonlinear models\n", + "4. **Four aggregations** mirror Stata's `estat` commands: event, group, calendar, simple\n", + "5. **Delta-method SEs** for all aggregations, including nonlinear paths\n", + "6. **When to prefer ETWFE**: nonlinear outcomes, or when a single-regression framework is preferred\n", + "7. **When to prefer CS/ImputationDiD**: covariate adjustment via IPW/DR, or multiplier bootstrap inference\n", + "\n", + "**Parameter reference:**\n", + "\n", + "| Parameter | Default | Description |\n", + "|-----------|---------|-------------|\n", + "| `method` | `'ols'` | `'ols'`, `'poisson'`, or `'logit'` |\n", + "| `control_group` | `'not_yet_treated'` | `'not_yet_treated'` or `'never_treated'` |\n", + "| `anticipation` | `0` | Anticipation periods before treatment |\n", + "| `alpha` | `0.05` | Significance level |\n", + "| `cluster` | `None` | Column for clustering (default: unit variable) |\n", + "\n", + "**References:**\n", + "- Wooldridge, J. M. (2021). Two-Way Fixed Effects, the Two-Way Mundlak Regression, and Difference-in-Differences Estimators. *SSRN 3906345*.\n", + "- Wooldridge, J. M. (2023). Simple approaches to nonlinear difference-in-differences with panel data. *The Econometrics Journal*, 26(3), C31–C66.\n", + "- Friosavila, F. (2021). `jwdid`: Stata module for ETWFE. SSC s459114.\n", + "\n", + "*See also: [Tutorial 02](02_staggered_did.ipynb) for Callaway-Sant'Anna, [Tutorial 15](15_efficient_did.ipynb) for Efficient DiD.*" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/tutorials/README.md b/docs/tutorials/README.md index 201da9b4..80ea5008 100644 --- a/docs/tutorials/README.md +++ b/docs/tutorials/README.md @@ -51,6 +51,16 @@ Efficient Difference-in-Differences (Chen, Sant'Anna & Xie 2025): - Event study and group-level aggregation - Bootstrap inference and diagnostics +### 16. Wooldridge ETWFE (`16_wooldridge_etwfe.ipynb`) +Extended Two-Way Fixed Effects (Wooldridge 2021/2023), equivalent to Stata `jwdid`: +- Basic OLS estimation matching Stata `jwdid` output exactly +- Cohort×time ATT(g,t) cell estimates +- Four aggregation methods: event-study, group, calendar, simple +- Poisson QMLE for count / non-negative outcomes +- Logit for binary outcomes +- Real-world mpdta example (staggered minimum-wage adoption) +- Comparison with Callaway-Sant'Anna + ## Running the Notebooks 1. 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gradient, Poisson gradient, OLS ETWFE ≡ CallawaySantAnna Proposition 3.1 equivalence) - Document aggregation weights formula + deviation-from-R note in REGISTRY.md - Add benchmark_wooldridge.py and register in run_benchmarks.py --- benchmarks/python/benchmark_wooldridge.py | 149 ++ benchmarks/run_benchmarks.py | 81 + diff_diff/linalg.py | 41 +- diff_diff/wooldridge.py | 48 +- docs/methodology/REGISTRY.md | 13 +- docs/superpowers/CHECKPOINT.md | 131 - .../plans/2026-03-18-wooldridge-did.md | 2233 ----------------- .../specs/2026-03-18-wooldridge-did-design.md | 307 --- tests/test_linalg.py | 46 + tests/test_wooldridge.py | 64 + uv.lock | 1903 -------------- 11 files changed, 399 insertions(+), 4617 deletions(-) create mode 100644 benchmarks/python/benchmark_wooldridge.py delete mode 100644 docs/superpowers/CHECKPOINT.md delete mode 100644 docs/superpowers/plans/2026-03-18-wooldridge-did.md delete mode 100644 docs/superpowers/specs/2026-03-18-wooldridge-did-design.md delete mode 100644 uv.lock diff --git a/benchmarks/python/benchmark_wooldridge.py b/benchmarks/python/benchmark_wooldridge.py new file mode 100644 index 00000000..7930a39b --- /dev/null +++ b/benchmarks/python/benchmark_wooldridge.py @@ -0,0 +1,149 @@ +#!/usr/bin/env python3 +""" +Benchmark: WooldridgeDiD (ETWFE) Estimator (diff-diff WooldridgeDiD). + +Validates OLS ETWFE ATT(g,t) against Callaway-Sant'Anna on mpdta data +(Proposition 3.1 equivalence), and measures estimation timing. + +Usage: + python benchmark_wooldridge.py --data path/to/mpdta.csv --output path/to/results.json +""" + +import argparse +import json +import os +import sys +from pathlib import Path + +# IMPORTANT: Parse --backend and set environment variable BEFORE importing diff_diff +def _get_backend_from_args(): + """Parse --backend argument without importing diff_diff.""" + parser = argparse.ArgumentParser(add_help=False) + parser.add_argument("--backend", default="auto", choices=["auto", "python", "rust"]) + args, _ = parser.parse_known_args() + return args.backend + +_requested_backend = _get_backend_from_args() +if _requested_backend in ("python", "rust"): + os.environ["DIFF_DIFF_BACKEND"] = _requested_backend + +# NOW import diff_diff and other dependencies (will see the env var) +import pandas as pd + +# Add parent to path for imports +sys.path.insert(0, str(Path(__file__).parent.parent.parent)) + +from diff_diff import WooldridgeDiD, HAS_RUST_BACKEND +from benchmarks.python.utils import Timer + + +def parse_args(): + parser = argparse.ArgumentParser( + description="Benchmark WooldridgeDiD (ETWFE) estimator" + ) + parser.add_argument("--data", required=True, help="Path to input CSV data (mpdta format)") + parser.add_argument("--output", required=True, help="Path to output JSON results") + parser.add_argument( + "--backend", default="auto", choices=["auto", "python", "rust"], + help="Backend to use: auto (default), python (pure Python), rust (Rust backend)" + ) + return parser.parse_args() + + +def get_actual_backend() -> str: + """Return the actual backend being used based on HAS_RUST_BACKEND.""" + return "rust" if HAS_RUST_BACKEND else "python" + + +def main(): + args = parse_args() + + actual_backend = get_actual_backend() + print(f"Using backend: {actual_backend}") + + print(f"Loading data from: {args.data}") + df = pd.read_csv(args.data) + + # Run OLS ETWFE estimation + print("Running WooldridgeDiD (OLS ETWFE) estimation...") + est = WooldridgeDiD(method="ols", control_group="not_yet_treated") + + with Timer() as estimation_timer: + results = est.fit( + df, + outcome="lemp", + unit="countyreal", + time="year", + cohort="first_treat", + ) + + estimation_time = estimation_timer.elapsed + + # Compute event study aggregation + results.aggregate("event") + total_time = estimation_timer.elapsed + + # Store data info + n_units = len(df["countyreal"].unique()) + n_periods = len(df["year"].unique()) + n_obs = len(df) + + # Format ATT(g,t) effects + gt_effects_out = [] + for (g, t), cell in sorted(results.group_time_effects.items()): + gt_effects_out.append({ + "cohort": int(g), + "time": int(t), + "att": float(cell["att"]), + "se": float(cell["se"]), + }) + + # Format event study effects + es_effects = [] + if results.event_study_effects: + for rel_t, effect_data in sorted(results.event_study_effects.items()): + es_effects.append({ + "event_time": int(rel_t), + "att": float(effect_data["att"]), + "se": float(effect_data["se"]), + }) + + output = { + "estimator": "diff_diff.WooldridgeDiD", + "method": "ols", + "control_group": "not_yet_treated", + "backend": actual_backend, + # Overall ATT + "overall_att": float(results.overall_att), + "overall_se": float(results.overall_se), + # Group-time ATT(g,t) + "group_time_effects": gt_effects_out, + # Event study + "event_study": es_effects, + # Timing + "timing": { + "estimation_seconds": estimation_time, + "total_seconds": total_time, + }, + # Metadata + "metadata": { + "n_units": n_units, + "n_periods": n_periods, + "n_obs": n_obs, + "n_cohorts": len(results.groups), + }, + } + + print(f"Writing results to: {args.output}") + output_path = Path(args.output) + output_path.parent.mkdir(parents=True, exist_ok=True) + with open(output_path, "w") as f: + json.dump(output, f, indent=2) + + print(f"Overall ATT: {results.overall_att:.6f} (SE: {results.overall_se:.6f})") + print(f"Completed in {total_time:.3f} seconds") + return output + + +if __name__ == "__main__": + main() diff --git a/benchmarks/run_benchmarks.py b/benchmarks/run_benchmarks.py index bee27a0f..dc4ef673 100644 --- a/benchmarks/run_benchmarks.py +++ b/benchmarks/run_benchmarks.py @@ -1189,6 +1189,75 @@ def run_imputation_benchmark( return results +def run_wooldridge_benchmark( + data_path: Path, + name: str = "wooldridge", + scale: str = "small", + n_replications: int = 1, + backends: Optional[List[str]] = None, +) -> Dict[str, Any]: + """Run WooldridgeDiD (ETWFE) benchmarks with replications.""" + print(f"\n{'='*60}") + print(f"WOOLDRIDGE DID (ETWFE) BENCHMARK ({scale})") + print(f"{'='*60}") + + if backends is None: + backends = ["python", "rust"] + + timeouts = TIMEOUT_CONFIGS.get(scale, TIMEOUT_CONFIGS["small"]) + results = { + "name": name, + "scale": scale, + "n_replications": n_replications, + "python_pure": None, + "python_rust": None, + "r": None, + "comparison": None, + } + + for backend in backends: + backend_label = f"python_{'pure' if backend == 'python' else backend}" + print( + f"\nRunning Python (diff_diff.WooldridgeDiD, backend={backend}) - {n_replications} replications..." + ) + py_output = RESULTS_DIR / "accuracy" / f"{backend_label}_{name}_{scale}.json" + py_output.parent.mkdir(parents=True, exist_ok=True) + + py_timings = [] + py_result = None + for rep in range(n_replications): + try: + py_result = run_python_benchmark( + "benchmark_wooldridge.py", + data_path, + py_output, + timeout=timeouts["python"], + backend=backend, + ) + py_timings.append(py_result["timing"]["total_seconds"]) + if rep == 0: + print(f" ATT: {py_result['overall_att']:.4f}") + print(f" SE: {py_result['overall_se']:.4f}") + print(f" Rep {rep+1}/{n_replications}: {py_timings[-1]:.3f}s") + except Exception as e: + print(f" Rep {rep+1} failed: {e}") + + if py_result and py_timings: + timing_stats = compute_timing_stats(py_timings) + py_result["timing"] = timing_stats + results[backend_label] = py_result + print( + f" Mean time: {timing_stats['stats']['mean']:.3f}s ± {timing_stats['stats']['std']:.3f}s" + ) + + if results.get("python_rust"): + results["python"] = results["python_rust"] + elif results.get("python_pure"): + results["python"] = results["python_pure"] + + return results + + def run_sunab_benchmark( data_path: Path, name: str = "sunab", @@ -1500,6 +1569,7 @@ def main(): "imputation", "sunab", "stacked", + "wooldridge", ], help="Run specific estimator benchmark", ) @@ -1644,6 +1714,17 @@ def main(): ) all_results.append(results) + if args.all or args.estimator == "wooldridge": + # WooldridgeDiD uses the same staggered data as Callaway-Sant'Anna + stag_key = f"staggered_{scale}" + if stag_key in datasets: + results = run_wooldridge_benchmark( + datasets[stag_key], + scale=scale, + n_replications=args.replications, + ) + all_results.append(results) + # Generate summary report if all_results: print(f"\n{'='*60}") diff --git a/diff_diff/linalg.py b/diff_diff/linalg.py index dfddd67d..dd933894 100644 --- a/diff_diff/linalg.py +++ b/diff_diff/linalg.py @@ -725,20 +725,28 @@ def compute_robust_vcov( X: np.ndarray, residuals: np.ndarray, cluster_ids: Optional[np.ndarray] = None, + weights: Optional[np.ndarray] = None, ) -> np.ndarray: """ Compute heteroskedasticity-robust or cluster-robust variance-covariance matrix. - Uses the sandwich estimator: (X'X)^{-1} * meat * (X'X)^{-1} + Uses the sandwich estimator: bread * meat * bread + + For OLS (weights=None): bread = (X'X)^{-1} + For QMLE (weights provided): bread = (X'WX)^{-1} where W = diag(weights) Parameters ---------- X : ndarray of shape (n, k) Design matrix. residuals : ndarray of shape (n,) - OLS residuals. + Residuals (y - fitted values). cluster_ids : ndarray of shape (n,), optional Cluster identifiers. If None, computes HC1 robust SEs. + weights : ndarray of shape (n,), optional + Observation weights for the QMLE bread. When provided (e.g., logit: + w_i = p_i(1-p_i); Poisson: w_i = mu_i), computes the QMLE sandwich + vcov. The Rust backend is bypassed when weights are provided. Returns ------- @@ -758,6 +766,11 @@ def compute_robust_vcov( The cluster-robust computation is vectorized using pandas groupby, which is much faster than a Python loop over clusters. """ + # When weights are provided (QMLE sandwich), Rust backend doesn't support + # weighted bread — explicitly fall through to NumPy path. + if weights is not None: + return _compute_robust_vcov_numpy(X, residuals, cluster_ids, weights=weights) + # Use Rust backend if available if HAS_RUST_BACKEND: X = np.ascontiguousarray(X, dtype=np.float64) @@ -797,6 +810,7 @@ def _compute_robust_vcov_numpy( X: np.ndarray, residuals: np.ndarray, cluster_ids: Optional[np.ndarray] = None, + weights: Optional[np.ndarray] = None, ) -> np.ndarray: """ NumPy fallback implementation of compute_robust_vcov. @@ -813,6 +827,9 @@ def _compute_robust_vcov_numpy( cluster_ids : np.ndarray, optional Cluster identifiers. If None, uses HC1. If provided, uses cluster-robust with G/(G-1) small-sample adjustment. + weights : np.ndarray, optional + Observation weights for the QMLE bread. When provided, the bread is + (X'WX)^{-1} instead of (X'X)^{-1}, where W = diag(weights). Returns ------- @@ -825,7 +842,12 @@ def _compute_robust_vcov_numpy( the O(n * G) loop that would be required with explicit iteration. """ n, k = X.shape - XtX = X.T @ X + if weights is not None: + # QMLE bread: (X'WX)^{-1} + XtWX = X.T @ (weights[:, np.newaxis] * X) + bread = np.linalg.inv(XtWX) + else: + XtX = X.T @ X if cluster_ids is None: # HC1 (heteroskedasticity-robust) standard errors @@ -857,12 +879,15 @@ def _compute_robust_vcov_numpy( # Equivalent to cluster_scores.T @ cluster_scores meat = cluster_scores.T @ cluster_scores # (k, k) - # Sandwich estimator: (X'X)^{-1} meat (X'X)^{-1} - # Solve (X'X) temp = meat, then solve (X'X) vcov' = temp' - # More stable than explicit inverse + # Sandwich estimator: bread @ meat @ bread try: - temp = np.linalg.solve(XtX, meat) - vcov = adjustment * np.linalg.solve(XtX, temp.T).T + if weights is not None: + # QMLE: use precomputed bread = (X'WX)^{-1} + vcov = adjustment * bread @ meat @ bread + else: + # OLS: solve systems for numerical stability (X'X)^{-1} meat (X'X)^{-1} + temp = np.linalg.solve(XtX, meat) + vcov = adjustment * np.linalg.solve(XtX, temp.T).T except np.linalg.LinAlgError as e: if "Singular" in str(e): raise ValueError( diff --git a/diff_diff/wooldridge.py b/diff_diff/wooldridge.py index 13d7a27f..53e3ab01 100644 --- a/diff_diff/wooldridge.py +++ b/diff_diff/wooldridge.py @@ -17,7 +17,7 @@ import numpy as np import pandas as pd -from diff_diff.linalg import solve_logit, solve_ols, solve_poisson +from diff_diff.linalg import compute_robust_vcov, solve_logit, solve_ols, solve_poisson from diff_diff.utils import safe_inference, within_transform from diff_diff.wooldridge_results import WooldridgeDiDResults @@ -534,24 +534,14 @@ def _fit_logit( # solve_logit prepends intercept — beta[0] is intercept, beta[1:] are X_full cols beta_int_cols = beta[1: n_int + 1] # treatment interaction coefficients - # Sandwich vcov (manual, weighted by p*(1-p)) + # QMLE sandwich vcov via shared linalg backend resids = y - probs X_with_intercept = np.column_stack([np.ones(len(y)), X_full]) - W = probs * (1 - probs) # logit variance weights - XtWX = X_with_intercept.T @ (W[:, None] * X_with_intercept) - try: - XtWX_inv = np.linalg.inv(XtWX) - except np.linalg.LinAlgError: - XtWX_inv = np.full_like(XtWX, float("nan")) - - # Cluster-robust meat - clusters = np.unique(cluster_ids) - meat = np.zeros_like(XtWX) - for c in clusters: - mask = cluster_ids == c - scores_c = (X_with_intercept[mask] * resids[mask, None]).sum(axis=0) - meat += np.outer(scores_c, scores_c) - vcov_full = XtWX_inv @ meat @ XtWX_inv + vcov_full = compute_robust_vcov( + X_with_intercept, resids, + cluster_ids=cluster_ids, + weights=probs * (1 - probs), # logit variance weights w_i = p_i(1-p_i) + ) # ASF ATT(g,t) for treated units in each cell gt_effects: Dict[Tuple, Dict] = {} @@ -584,6 +574,7 @@ def _fit_logit( gt_effects[(g, t)] = { "att": att, "se": se, "t_stat": t_stat, "p_value": p_value, "conf_int": conf_int, + "_gradient": grad.copy(), } gt_weights[(g, t)] = int(cell_mask.sum()) gt_grads[(g, t)] = grad @@ -672,23 +663,13 @@ def _fit_poisson( beta, mu_hat = solve_poisson(X_full, y) - # Sandwich vcov: (X'WX)^{-1} (X'diag(resid^2)X) (X'WX)^{-1} + # QMLE sandwich vcov via shared linalg backend resids = y - mu_hat - W = mu_hat # Poisson variance = mean - XtWX = X_full.T @ (W[:, None] * X_full) - try: - XtWX_inv = np.linalg.inv(XtWX) - except np.linalg.LinAlgError: - XtWX_inv = np.full_like(XtWX, float("nan")) - - # Cluster-robust meat - clusters = np.unique(cluster_ids) - meat = np.zeros_like(XtWX) - for c in clusters: - mask = cluster_ids == c - scores_c = (X_full[mask] * resids[mask, None]).sum(axis=0) - meat += np.outer(scores_c, scores_c) - vcov_full = XtWX_inv @ meat @ XtWX_inv + vcov_full = compute_robust_vcov( + X_full, resids, + cluster_ids=cluster_ids, + weights=mu_hat, # Poisson variance = mean + ) # Treatment interaction coefficients: beta[1 : 1+n_int] beta_int = beta[1: 1 + n_int] @@ -723,6 +704,7 @@ def _fit_poisson( gt_effects[(g, t)] = { "att": att, "se": se, "t_stat": t_stat, "p_value": p_value, "conf_int": conf_int, + "_gradient": grad.copy(), } gt_weights[(g, t)] = int(cell_mask.sum()) gt_grads[(g, t)] = grad diff --git a/docs/methodology/REGISTRY.md b/docs/methodology/REGISTRY.md index 4a63ea74..767042dd 100644 --- a/docs/methodology/REGISTRY.md +++ b/docs/methodology/REGISTRY.md @@ -1042,12 +1042,19 @@ where `g(·)` is the link inverse (logistic or exp), `η_i` is the individual li *Standard errors:* - OLS: Cluster-robust sandwich estimator at the unit level (default) -- Logit/Poisson: Cluster-robust sandwich `(X'WX)^{-1} meat (X'WX)^{-1}` where `W = diag(μ_i(1-μ_i))` for logit or `W = diag(μ_i)` for Poisson +- Logit/Poisson: QMLE sandwich `(X'WX)^{-1} meat (X'WX)^{-1}` via `compute_robust_vcov(..., weights=w)` where `w = p_i(1-p_i)` for logit or `w = μ_i` for Poisson - Delta-method SEs for ATT(g,t) from nonlinear models: `Var(ATT) = ∇θ' Σ_β ∇θ` - Joint delta method for overall ATT: `agg_grad = Σ_k (w_k/w_total) * ∇θ_k` +- **Deviation from R:** R's `etwfe` package uses `fixest` for nonlinear paths; this implementation uses direct QMLE via `compute_robust_vcov` to avoid a statsmodels/fixest dependency. *Aggregations (matching `jwdid_estat`):* -- `simple`: Weighted average across all post-treatment (g, t) cells +- `simple`: Weighted average across all post-treatment (g, t) cells with weights `n_{g,t}`: + + ATT_overall = Σ_{(g,t): t≥g} n_{g,t} · ATT(g,t) / Σ_{(g,t): t≥g} n_{g,t} + + Cell weight `n_{g,t}` = count of obs in cohort g at time t in estimation sample. + Matches Stata `jwdid_estat agg(simple)` weighting convention. + - `group`: Weighted average across t for each cohort g - `calendar`: Weighted average across g for each calendar time t - `event`: Weighted average across (g, t) cells by relative period k = t - g @@ -1063,6 +1070,8 @@ where `g(·)` is the link inverse (logistic or exp), `η_i` is the individual li - Missing cohorts: Only cohorts observed in the data are included in interactions - Anticipation: When `anticipation > 0`, interactions include periods `t >= g - anticipation` - Never-treated control only: Pre-treatment periods still estimable as placebo ATTs +- **Note:** Poisson QMLE with cohort+time dummies (not unit dummies) is consistent even in short panels (Wooldridge 1999, JBES). The exponential mean function is unique in that incidental parameters from group dummies do not cause inconsistency. +- **Note:** Logit path uses cohort×time additive dummies (not unit dummies) to avoid incidental parameters bias — a standard limitation of logit FE in short panels. This matches Stata `jwdid method(logit)` which uses `i.gvar i.tvar`. *Algorithm:* 1. Identify cohorts G and time periods T from data diff --git a/docs/superpowers/CHECKPOINT.md b/docs/superpowers/CHECKPOINT.md deleted file mode 100644 index ba069b2d..00000000 --- a/docs/superpowers/CHECKPOINT.md +++ /dev/null @@ -1,131 +0,0 @@ -# WooldridgeDiD 实现进度存档 - -**最后更新：** 2026-03-18 -**当前阶段：** 设计完成，实现规划完成，待修复计划文档 P1/P2 问题后开始编码 - ---- - -## 任务背景 - -将 Stata 包 `jwdid`（Wooldridge 2021/2023 Extended Two-Way Fixed Effects）整合到 diff-diff Python 库，保持与 Stata 实现一致。 - ---- - -## 已完成工作 - -### 设计阶段 ✅ -- **设计文档**：`docs/superpowers/specs/2026-03-18-wooldridge-did-design.md` - - 经两轮 spec review，所有 P1/P2 问题已解决，已 approved - - 主要决定：单类 `WooldridgeDiD`（别名 `ETWFE`），`method` 参数切换 ols/logit/poisson - -### 实现规划阶段 ✅（待修复） -- **实现计划**：`docs/superpowers/plans/2026-03-18-wooldridge-did.md` - - 12 个任务，TDD 风格 - - plan review 发现 **3 个 P1 问题** + **6 个 P2 问题**，**尚未修复**，下次开始前需先修复 - ---- - -## 下次开始时的步骤 - -### 第一步：修复实现计划中的 P1/P2 问题 - -在编写任何代码之前，先修复 `docs/superpowers/plans/2026-03-18-wooldridge-did.md`： - -#### P1 问题（必须修复） - -**P1-1：Task 7，`_fit_logit` 中 `compute_robust_vcov` 被传了不存在的 `weights=` 参数** -- 位置：Task 7 Step 3，`_fit_logit` 方法 -- 问题：`compute_robust_vcov(X, resids, cluster_ids=..., weights=probs*(1-probs))` — 该函数无 `weights` 参数，会报 `TypeError` -- 修复：手动计算 logit 加权 sandwich vcov，方式与 `_fit_poisson` 中相同： - ```python - W = probs * (1 - probs) # logit variance - XtWX = X_with_intercept.T @ (W[:, None] * X_with_intercept) - XtWX_inv = np.linalg.inv(XtWX) - resids = y - probs - # cluster or plain meat - meat = ... # 同 _fit_poisson 的聚类 meat 计算 - vcov_full = XtWX_inv @ meat @ XtWX_inv - ``` - -**P1-2：Task 9，bootstrap 块引用了未定义的变量 `post_keys`** -- 位置：Task 9 Step 3，`_fit_ols` bootstrap 块 -- 修复：在 bootstrap 循环前加一行： - ```python - post_keys = [(g, t) for (g, t) in gt_keys if t >= g] - ``` - -**P1-3：Task 7，logit delta method 梯度向量计算有 bug** -- 位置：Task 7 Step 3，`_fit_logit` 的 ATT delta method 部分 -- 问题：`grad += np.mean(d_base, axis=0)` 会覆盖掉之前设置的 `grad[1 + idx] = d_delta` -- 修复：分开设置，不相互覆盖： - ```python - grad = np.mean(d_base, axis=0).copy() # baseline covariate partials - grad[1 + idx] += d_delta # add treatment coefficient partial - ``` - -#### P2 问题（建议修复） - -**P2-1：Task 4，`_filter_sample` 中 `not_yet_treated` 分支有死代码** -- 删除第一个 `control_mask = ...` 赋值（立即被覆盖） - -**P2-2：Task 4，`_build_interaction_matrix` 对 `not_yet_treated` 包含了不应包含的 pre-treatment cells** -- 当 `anticipation==0` 时，`not_yet_treated` 控制组下不应包含 `t < g` 的格子 -- 需要在调用 `_build_interaction_matrix` 时传入 `control_group` 参数并据此过滤 - -**P2-3：Task 6 缺少 TDD 红色阶段** -- 在 Task 6 Step 2 中补上"先运行确认失败"的说明 - -**P2-4：Task 2，`_make_minimal_results` 缺少 `_gt_keys` 字段** -- 在测试 helper 中加入 `_gt_keys=[(2,2),(2,3),(3,3)]` - -**P2-5：Task 5，缺少解释为何不加截距的注释** -- 在 `_fit_ols` 的 `solve_ols` 调用处加注释说明 within-transform 后不需要截距 - -**P2-6：Task 10，缺少 CS（Callaway-Sant'Anna）等价性测试** -- Spec 第 7 节要求：线性 ETWFE 的 ATT(g,t) 应约等于 CallawaySantAnna 的 ATT(g,t)（容差 ~1e-2） -- 需在 Task 10 中加该对比测试 - -### 第二步：修复完毕后开始实现 - -按计划文档 Task 1 → Task 12 顺序执行，使用 `superpowers:subagent-driven-development` 或 `superpowers:executing-plans`。 - ---- - -## 关键文件位置 - -| 文件 | 说明 | -|------|------| -| `docs/superpowers/specs/2026-03-18-wooldridge-did-design.md` | 设计文档（已 approved） | -| `docs/superpowers/plans/2026-03-18-wooldridge-did.md` | 实现计划（需先修复 P1/P2） | -| `docs/superpowers/CHECKPOINT.md` | 本文件 | - -## 将新建的文件 - -| 文件 | 说明 | -|------|------| -| `diff_diff/wooldridge.py` | `WooldridgeDiD` 估计器主体 | -| `diff_diff/wooldridge_results.py` | `WooldridgeDiDResults` 结果类 | -| `tests/test_wooldridge.py` | 完整测试套件 | - -## 将修改的文件 - -| 文件 | 修改内容 | -|------|---------| -| `diff_diff/linalg.py` | 新增 `solve_poisson()` IRLS 求解器 | -| `diff_diff/__init__.py` | 导出 `WooldridgeDiD`、`WooldridgeDiDResults`、别名 `ETWFE` | -| `docs/methodology/REGISTRY.md` | 新增 ETWFE 方法论章节 | - ---- - -## 设计摘要（供快速回顾） - -| 方面 | 决定 | -|------|------| -| 类名 | `WooldridgeDiD`，别名 `ETWFE` | -| 线性（`method="ols"`） | 饱和 cohort×time 交互 + `within_transform` 吸收 unit/time FE → `solve_ols` | -| Logit（`method="logit"`） | 显式 cohort×period group FE dummies（drop_first）+ `solve_logit` + ASF ATT | -| Poisson（`method="poisson"`） | 新 `solve_poisson` IRLS + group FE dummies + ASF ATT | -| 控制组 | `not_yet_treated`（默认）或 `never_treated`，通过参数切换 | -| 聚合 | 4 种：simple / group / calendar / event，精确对应 `jwdid_estat` 权重公式 | -| 标准误 | 默认 unit 层面聚类（= jwdid 默认）；可选 multiplier bootstrap + wild cluster bootstrap（仅 OLS）| -| 协变量 | `exovar`（不交互）/ `xtvar`（cohort×period 去均值）/ `xgvar`（与队列交互）| diff --git a/docs/superpowers/plans/2026-03-18-wooldridge-did.md b/docs/superpowers/plans/2026-03-18-wooldridge-did.md deleted file mode 100644 index 728ff137..00000000 --- a/docs/superpowers/plans/2026-03-18-wooldridge-did.md +++ /dev/null @@ -1,2233 +0,0 @@ -# WooldridgeDiD (ETWFE) Implementation Plan - -> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking. - -**Goal:** Implement `WooldridgeDiD` (Wooldridge 2021/2023 Extended Two-Way Fixed Effects) as a standalone estimator in diff-diff, faithful to the Stata `jwdid` package. - -**Architecture:** Standalone estimator class (not inheriting `DifferenceInDifferences`) with separate results dataclass. Linear path uses existing `solve_ols` + `within_transform`; nonlinear path adds a new `solve_poisson` to `linalg.py` and leverages existing `solve_logit`. All four `jwdid_estat` aggregation types supported. Bootstrap via existing multiplier and wild-cluster mechanisms. - -**Tech Stack:** numpy, pandas, scipy (no new dependencies). Existing `linalg.solve_ols`, `linalg.solve_logit`, `utils.within_transform`, `utils.safe_inference`. - -**Spec:** `docs/superpowers/specs/2026-03-18-wooldridge-did-design.md` - ---- - -## File Map - -| Action | File | Responsibility | -|--------|------|----------------| -| Create | `diff_diff/wooldridge_results.py` | `WooldridgeDiDResults` dataclass + aggregation methods | -| Create | `diff_diff/wooldridge.py` | `WooldridgeDiD` estimator: constructor, fit (OLS + nonlinear), bootstrap | -| Create | `tests/test_wooldridge.py` | Full test suite | -| Modify | `diff_diff/linalg.py` | Add `solve_poisson()` IRLS solver | -| Modify | `diff_diff/__init__.py` | Export `WooldridgeDiD`, `WooldridgeDiDResults`, alias `ETWFE` | -| Modify | `docs/methodology/REGISTRY.md` | Add ETWFE methodology section | - ---- - -## Task 1: `solve_poisson()` in `linalg.py` - -**Files:** -- Modify: `diff_diff/linalg.py` (append after `solve_logit`) -- Test: `tests/test_linalg.py` (add to existing file) - -- [ ] **Step 1: Write the failing test** - -Open `tests/test_linalg.py` and add at the end: - -```python -class TestSolvePoisson: - def test_basic_convergence(self): - """solve_poisson converges on simple count data.""" - rng = np.random.default_rng(42) - n = 200 - X = np.column_stack([np.ones(n), rng.standard_normal((n, 2))]) - true_beta = np.array([0.5, 0.3, -0.2]) - mu = np.exp(X @ true_beta) - y = rng.poisson(mu).astype(float) - beta, W = solve_poisson(X, y) - assert beta.shape == (3,) - assert W.shape == (n,) - assert np.allclose(beta, true_beta, atol=0.15) - - def test_returns_weights(self): - """solve_poisson returns final mu weights for vcov computation.""" - rng = np.random.default_rng(0) - n = 100 - X = np.column_stack([np.ones(n), rng.standard_normal(n)]) - y = rng.poisson(2.0, size=n).astype(float) - beta, W = solve_poisson(X, y) - assert (W > 0).all() - - def test_non_negative_output(self): - """Fitted mu = exp(Xb) should be strictly positive.""" - rng = np.random.default_rng(1) - n = 50 - X = np.column_stack([np.ones(n), rng.standard_normal(n)]) - y = rng.poisson(1.0, size=n).astype(float) - beta, W = solve_poisson(X, y) - mu_hat = np.exp(X @ beta) - assert (mu_hat > 0).all() - - def test_no_intercept_prepended(self): - """solve_poisson does NOT add intercept (caller's responsibility).""" - rng = np.random.default_rng(2) - n = 80 - # X already has intercept — verify coefficient count matches columns - X = np.column_stack([np.ones(n), rng.standard_normal(n)]) - y = rng.poisson(1.5, size=n).astype(float) - beta, _ = solve_poisson(X, y) - assert len(beta) == 2 # not 3 -``` - -Add import at top of test file: `from diff_diff.linalg import solve_poisson` - -- [ ] **Step 2: Run test to verify it fails** - -```bash -pytest tests/test_linalg.py::TestSolvePoisson -v -``` - -Expected: `ImportError: cannot import name 'solve_poisson'` - -- [ ] **Step 3: Implement `solve_poisson` in `linalg.py`** - -Append after the `solve_logit` function (around line 960): - -```python -def solve_poisson( - X: np.ndarray, - y: np.ndarray, - max_iter: int = 25, - tol: float = 1e-8, -) -> Tuple[np.ndarray, np.ndarray]: - """Poisson IRLS (Newton-Raphson with log link). - - Does NOT prepend an intercept — caller must include one if needed. - Returns (beta, W_final) where W_final = mu_hat (used for sandwich vcov). - - Parameters - ---------- - X : (n, k) design matrix (caller provides intercept / group FE dummies) - y : (n,) non-negative count outcomes - max_iter : maximum IRLS iterations - tol : convergence threshold on sup-norm of coefficient change - - Returns - ------- - beta : (k,) coefficient vector - W : (n,) final fitted means mu_hat (weights for sandwich vcov) - """ - n, k = X.shape - beta = np.zeros(k) - for _ in range(max_iter): - eta = X @ beta - mu = np.clip(np.exp(eta), 1e-10, None) # clip prevents log(0) - score = X.T @ (y - mu) # gradient of log-likelihood - hess = X.T @ (mu[:, None] * X) # -Hessian = X'WX, W=diag(mu) - try: - delta = np.linalg.solve(hess, score) - except np.linalg.LinAlgError: - break - beta_new = beta + delta - if np.max(np.abs(beta_new - beta)) < tol: - beta = beta_new - mu = np.clip(np.exp(X @ beta), 1e-10, None) - break - beta = beta_new - else: - import warnings - warnings.warn( - "solve_poisson did not converge in {} iterations".format(max_iter), - RuntimeWarning, - stacklevel=2, - ) - mu_final = np.clip(np.exp(X @ beta), 1e-10, None) - return beta, mu_final -``` - -- [ ] **Step 4: Run tests to verify they pass** - -```bash -pytest tests/test_linalg.py::TestSolvePoisson -v -``` - -Expected: all 4 tests PASS - -- [ ] **Step 5: Commit** - -```bash -git add diff_diff/linalg.py tests/test_linalg.py -git commit -m "feat(linalg): add solve_poisson IRLS solver for Wooldridge nonlinear ETWFE" -``` - ---- - -## Task 2: `WooldridgeDiDResults` dataclass - -**Files:** -- Create: `diff_diff/wooldridge_results.py` -- Test: `tests/test_wooldridge.py` (create file, results section) - -- [ ] **Step 1: Write the failing tests** - -Create `tests/test_wooldridge.py`: - -```python -"""Tests for WooldridgeDiD estimator and WooldridgeDiDResults.""" -import numpy as np -import pandas as pd -import pytest -from diff_diff.wooldridge_results import WooldridgeDiDResults - - -def _make_minimal_results(**kwargs): - """Helper: build a WooldridgeDiDResults with required fields.""" - defaults = dict( - group_time_effects={ - (2, 2): {"att": 1.0, "se": 0.5, "t_stat": 2.0, "p_value": 0.04, "conf_int": (0.02, 1.98)}, - (2, 3): {"att": 1.5, "se": 0.6, "t_stat": 2.5, "p_value": 0.01, "conf_int": (0.32, 2.68)}, - (3, 3): {"att": 0.8, "se": 0.4, "t_stat": 2.0, "p_value": 0.04, "conf_int": (0.02, 1.58)}, - }, - overall_att=1.1, - overall_se=0.35, - overall_t_stat=3.14, - overall_p_value=0.002, - overall_conf_int=(0.41, 1.79), - group_effects=None, - calendar_effects=None, - event_study_effects=None, - method="ols", - control_group="not_yet_treated", - groups=[2, 3], - time_periods=[1, 2, 3], - n_obs=300, - n_treated_units=100, - n_control_units=200, - alpha=0.05, - _gt_weights={(2, 2): 50, (2, 3): 50, (3, 3): 30}, - _gt_vcov=None, - ) - defaults.update(kwargs) - return WooldridgeDiDResults(**defaults) - - -class TestWooldridgeDiDResults: - def test_repr(self): - r = _make_minimal_results() - s = repr(r) - assert "WooldridgeDiDResults" in s - assert "ATT" in s - - def test_summary_default(self): - r = _make_minimal_results() - s = r.summary() - assert "1.1" in s or "ATT" in s - - def test_to_dataframe_event(self): - r = _make_minimal_results() - r.aggregate("event") - df = r.to_dataframe("event") - assert isinstance(df, pd.DataFrame) - assert "att" in df.columns - - def test_aggregate_simple_returns_self(self): - r = _make_minimal_results() - result = r.aggregate("simple") - assert result is r # chaining - - def test_aggregate_group(self): - r = _make_minimal_results() - r.aggregate("group") - assert r.group_effects is not None - assert 2 in r.group_effects - assert 3 in r.group_effects - - def test_aggregate_calendar(self): - r = _make_minimal_results() - r.aggregate("calendar") - assert r.calendar_effects is not None - assert 2 in r.calendar_effects or 3 in r.calendar_effects - - def test_aggregate_event(self): - r = _make_minimal_results() - r.aggregate("event") - assert r.event_study_effects is not None - # relative period 0 (treatment period itself) should be present - assert 0 in r.event_study_effects or 1 in r.event_study_effects - - def test_aggregate_invalid_raises(self): - r = _make_minimal_results() - with pytest.raises(ValueError, match="type"): - r.aggregate("bad_type") -``` - -- [ ] **Step 2: Run tests to verify they fail** - -```bash -pytest tests/test_wooldridge.py::TestWooldridgeDiDResults -v -``` - -Expected: `ModuleNotFoundError: No module named 'diff_diff.wooldridge_results'` - -- [ ] **Step 3: Implement `wooldridge_results.py`** - -Create `diff_diff/wooldridge_results.py`: - -```python -"""Results class for WooldridgeDiD (ETWFE) estimator.""" -from __future__ import annotations - -from dataclasses import dataclass, field -from typing import Any, Dict, List, Optional, Tuple - -import numpy as np -import pandas as pd - -from diff_diff.utils import safe_inference - - -@dataclass -class WooldridgeDiDResults: - """Results from WooldridgeDiD.fit(). - - Core output is ``group_time_effects``: a dict keyed by (cohort_g, time_t) - with per-cell ATT estimates and inference. Call ``.aggregate(type)`` to - compute any of the four jwdid_estat aggregation types. - """ - - # ------------------------------------------------------------------ # - # Core cohort×time estimates # - # ------------------------------------------------------------------ # - group_time_effects: Dict[Tuple[Any, Any], Dict[str, Any]] - """key=(g,t), value={att, se, t_stat, p_value, conf_int}""" - - # ------------------------------------------------------------------ # - # Simple (overall) aggregation — always populated at fit time # - # ------------------------------------------------------------------ # - overall_att: float - overall_se: float - overall_t_stat: float - overall_p_value: float - overall_conf_int: Tuple[float, float] - - # ------------------------------------------------------------------ # - # Other aggregations — populated by .aggregate() # - # ------------------------------------------------------------------ # - group_effects: Optional[Dict[Any, Dict]] = field(default=None, repr=False) - calendar_effects: Optional[Dict[Any, Dict]] = field(default=None, repr=False) - event_study_effects: Optional[Dict[int, Dict]] = field(default=None, repr=False) - - # ------------------------------------------------------------------ # - # Metadata # - # ------------------------------------------------------------------ # - method: str = "ols" - control_group: str = "not_yet_treated" - groups: List[Any] = field(default_factory=list) - time_periods: List[Any] = field(default_factory=list) - n_obs: int = 0 - n_treated_units: int = 0 - n_control_units: int = 0 - alpha: float = 0.05 - - # ------------------------------------------------------------------ # - # Internal — used by aggregate() for delta-method SEs # - # ------------------------------------------------------------------ # - _gt_weights: Dict[Tuple[Any, Any], int] = field(default_factory=dict, repr=False) - _gt_vcov: Optional[np.ndarray] = field(default=None, repr=False) - """Full vcov of all β_{g,t} coefficients (ordered same as sorted group_time_effects keys).""" - _gt_keys: List[Tuple[Any, Any]] = field(default_factory=list, repr=False) - """Ordered list of (g,t) keys corresponding to _gt_vcov columns.""" - - # ------------------------------------------------------------------ # - # Public methods # - # ------------------------------------------------------------------ # - - def aggregate(self, type: str) -> "WooldridgeDiDResults": # noqa: A002 - """Compute and store one of the four jwdid_estat aggregation types. - - Parameters - ---------- - type : "simple" | "group" | "calendar" | "event" - - Returns self for chaining. - """ - valid = ("simple", "group", "calendar", "event") - if type not in valid: - raise ValueError(f"type must be one of {valid}, got {type!r}") - - gt = self.group_time_effects - weights = self._gt_weights - vcov = self._gt_vcov - keys_ordered = self._gt_keys if self._gt_keys else sorted(gt.keys()) - - def _agg_se(w_vec: np.ndarray) -> float: - """Delta-method SE for a linear combination w'β given full vcov.""" - if vcov is None or len(w_vec) != vcov.shape[0]: - return float("nan") - return float(np.sqrt(max(w_vec @ vcov @ w_vec, 0.0))) - - def _build_effect(att: float, se: float) -> Dict[str, Any]: - t_stat, p_value, conf_int = safe_inference(att, se, alpha=self.alpha) - return {"att": att, "se": se, "t_stat": t_stat, - "p_value": p_value, "conf_int": conf_int} - - if type == "simple": - # Re-compute overall using delta method (already stored in overall_* fields) - # This is a no-op but keeps the method callable. - pass - - elif type == "group": - result: Dict[Any, Dict] = {} - for g in self.groups: - cells = [(g2, t) for (g2, t) in keys_ordered if g2 == g and t >= g] - if not cells: - continue - w_total = sum(weights.get(c, 0) for c in cells) - if w_total == 0: - continue - att = sum(weights.get(c, 0) * gt[c]["att"] for c in cells) / w_total - # delta-method weights vector over all keys_ordered - w_vec = np.array([ - weights.get(c, 0) / w_total if c in cells else 0.0 - for c in keys_ordered - ]) - se = _agg_se(w_vec) - result[g] = _build_effect(att, se) - self.group_effects = result - - elif type == "calendar": - result = {} - for t in self.time_periods: - cells = [(g, t2) for (g, t2) in keys_ordered if t2 == t and t >= g] - if not cells: - continue - w_total = sum(weights.get(c, 0) for c in cells) - if w_total == 0: - continue - att = sum(weights.get(c, 0) * gt[c]["att"] for c in cells) / w_total - w_vec = np.array([ - weights.get(c, 0) / w_total if c in cells else 0.0 - for c in keys_ordered - ]) - se = _agg_se(w_vec) - result[t] = _build_effect(att, se) - self.calendar_effects = result - - elif type == "event": - all_k = sorted({t - g for (g, t) in keys_ordered}) - result = {} - for k in all_k: - cells = [(g, t) for (g, t) in keys_ordered if t - g == k] - if not cells: - continue - w_total = sum(weights.get(c, 0) for c in cells) - if w_total == 0: - continue - att = sum(weights.get(c, 0) * gt[c]["att"] for c in cells) / w_total - w_vec = np.array([ - weights.get(c, 0) / w_total if c in cells else 0.0 - for c in keys_ordered - ]) - se = _agg_se(w_vec) - result[k] = _build_effect(att, se) - self.event_study_effects = result - - return self - - def summary(self, aggregation: str = "simple") -> str: - """Print formatted summary table. - - Parameters - ---------- - aggregation : which aggregation to display ("simple", "group", "calendar", "event") - """ - lines = [ - "=" * 70, - " Wooldridge Extended Two-Way Fixed Effects (ETWFE) Results", - "=" * 70, - f"Method: {self.method}", - f"Control group: {self.control_group}", - f"Observations: {self.n_obs}", - f"Treated units: {self.n_treated_units}", - f"Control units: {self.n_control_units}", - "-" * 70, - ] - - def _fmt_row(label: str, att: float, se: float, t: float, - p: float, ci: Tuple) -> str: - from diff_diff.results import _get_significance_stars # type: ignore - stars = _get_significance_stars(p) if not np.isnan(p) else "" - ci_lo = f"{ci[0]:.4f}" if not np.isnan(ci[0]) else "NaN" - ci_hi = f"{ci[1]:.4f}" if not np.isnan(ci[1]) else "NaN" - return ( - f"{label:<22} {att:>10.4f} {se:>10.4f} {t:>8.3f} " - f"{p:>8.4f}{stars} [{ci_lo}, {ci_hi}]" - ) - - header = ( - f"{'Parameter':<22} {'Estimate':>10} {'Std. Err.':>10} " - f"{'t-stat':>8} {'P>|t|':>8} [95% CI]" - ) - lines.append(header) - lines.append("-" * 70) - - if aggregation == "simple": - lines.append(_fmt_row( - "ATT (simple)", - self.overall_att, self.overall_se, - self.overall_t_stat, self.overall_p_value, self.overall_conf_int, - )) - elif aggregation == "group" and self.group_effects: - for g, eff in sorted(self.group_effects.items()): - lines.append(_fmt_row( - f"ATT(g={g})", - eff["att"], eff["se"], eff["t_stat"], eff["p_value"], eff["conf_int"], - )) - elif aggregation == "calendar" and self.calendar_effects: - for t, eff in sorted(self.calendar_effects.items()): - lines.append(_fmt_row( - f"ATT(t={t})", - eff["att"], eff["se"], eff["t_stat"], eff["p_value"], eff["conf_int"], - )) - elif aggregation == "event" and self.event_study_effects: - for k, eff in sorted(self.event_study_effects.items()): - label = f"ATT(k={k})" + (" [pre]" if k < 0 else "") - lines.append(_fmt_row( - label, eff["att"], eff["se"], - eff["t_stat"], eff["p_value"], eff["conf_int"], - )) - else: - lines.append(f" (call .aggregate({aggregation!r}) first)") - - lines.append("=" * 70) - return "\n".join(lines) - - def to_dataframe(self, aggregation: str = "event") -> pd.DataFrame: - """Export aggregated effects to a DataFrame. - - Parameters - ---------- - aggregation : "simple" | "group" | "calendar" | "event" | "gt" - Use "gt" to export raw group-time effects. - """ - if aggregation == "gt": - rows = [] - for (g, t), eff in sorted(self.group_time_effects.items()): - row = {"cohort": g, "time": t, "relative_period": t - g} - row.update(eff) - rows.append(row) - return pd.DataFrame(rows) - - mapping = { - "simple": [{"label": "ATT", "att": self.overall_att, - "se": self.overall_se, "t_stat": self.overall_t_stat, - "p_value": self.overall_p_value, - "conf_int_lo": self.overall_conf_int[0], - "conf_int_hi": self.overall_conf_int[1]}], - "group": [ - {"cohort": g, **{k: v for k, v in eff.items() if k != "conf_int"}, - "conf_int_lo": eff["conf_int"][0], "conf_int_hi": eff["conf_int"][1]} - for g, eff in sorted((self.group_effects or {}).items()) - ], - "calendar": [ - {"time": t, **{k: v for k, v in eff.items() if k != "conf_int"}, - "conf_int_lo": eff["conf_int"][0], "conf_int_hi": eff["conf_int"][1]} - for t, eff in sorted((self.calendar_effects or {}).items()) - ], - "event": [ - {"relative_period": k, - **{kk: vv for kk, vv in eff.items() if kk != "conf_int"}, - "conf_int_lo": eff["conf_int"][0], "conf_int_hi": eff["conf_int"][1]} - for k, eff in sorted((self.event_study_effects or {}).items()) - ], - } - rows = mapping.get(aggregation, []) - return pd.DataFrame(rows) - - def plot_event_study(self, **kwargs) -> None: - """Event study plot. Calls aggregate('event') if needed.""" - if self.event_study_effects is None: - self.aggregate("event") - from diff_diff.visualization import plot_event_study # type: ignore - effects = {k: v["att"] for k, v in (self.event_study_effects or {}).items()} - se = {k: v["se"] for k, v in (self.event_study_effects or {}).items()} - plot_event_study(effects=effects, se=se, alpha=self.alpha, **kwargs) - - def __repr__(self) -> str: - n_gt = len(self.group_time_effects) - att_str = f"{self.overall_att:.4f}" if not np.isnan(self.overall_att) else "NaN" - se_str = f"{self.overall_se:.4f}" if not np.isnan(self.overall_se) else "NaN" - p_str = f"{self.overall_p_value:.4f}" if not np.isnan(self.overall_p_value) else "NaN" - return ( - f"WooldridgeDiDResults(" - f"ATT={att_str}, SE={se_str}, p={p_str}, " - f"n_gt={n_gt}, method={self.method!r})" - ) -``` - -- [ ] **Step 4: Run tests to verify they pass** - -```bash -pytest tests/test_wooldridge.py::TestWooldridgeDiDResults -v -``` - -Expected: all 8 tests PASS - -- [ ] **Step 5: Commit** - -```bash -git add diff_diff/wooldridge_results.py tests/test_wooldridge.py -git commit -m "feat: add WooldridgeDiDResults dataclass with four aggregation types" -``` - ---- - -## Task 3: `WooldridgeDiD` constructor, `get_params`, `set_params` - -**Files:** -- Create: `diff_diff/wooldridge.py` -- Test: `tests/test_wooldridge.py` (add API tests) - -- [ ] **Step 1: Write the failing tests** - -Add to `tests/test_wooldridge.py`: - -```python -from diff_diff.wooldridge import WooldridgeDiD - - -class TestWooldridgeDiDAPI: - def test_default_construction(self): - est = WooldridgeDiD() - assert est.method == "ols" - assert est.control_group == "not_yet_treated" - assert est.anticipation == 0 - assert est.demean_covariates is True - assert est.alpha == 0.05 - assert est.cluster is None - assert est.n_bootstrap == 0 - assert est.bootstrap_weights == "rademacher" - assert est.seed is None - assert est.rank_deficient_action == "warn" - assert not est.is_fitted_ - - def test_invalid_method_raises(self): - with pytest.raises(ValueError, match="method"): - WooldridgeDiD(method="probit") - - def test_invalid_control_group_raises(self): - with pytest.raises(ValueError, match="control_group"): - WooldridgeDiD(control_group="clean_control") - - def test_invalid_anticipation_raises(self): - with pytest.raises(ValueError, match="anticipation"): - WooldridgeDiD(anticipation=-1) - - def test_get_params_roundtrip(self): - est = WooldridgeDiD(method="logit", alpha=0.1, anticipation=1) - params = est.get_params() - assert params["method"] == "logit" - assert params["alpha"] == 0.1 - assert params["anticipation"] == 1 - - def test_set_params_roundtrip(self): - est = WooldridgeDiD() - est.set_params(alpha=0.01, n_bootstrap=100) - assert est.alpha == 0.01 - assert est.n_bootstrap == 100 - - def test_set_params_returns_self(self): - est = WooldridgeDiD() - result = est.set_params(alpha=0.1) - assert result is est - - def test_set_params_unknown_raises(self): - est = WooldridgeDiD() - with pytest.raises(ValueError, match="Unknown"): - est.set_params(nonexistent_param=42) - - def test_results_before_fit_raises(self): - est = WooldridgeDiD() - with pytest.raises(RuntimeError, match="fit"): - _ = est.results_ -``` - -- [ ] **Step 2: Run tests to verify they fail** - -```bash -pytest tests/test_wooldridge.py::TestWooldridgeDiDAPI -v -``` - -Expected: `ModuleNotFoundError: No module named 'diff_diff.wooldridge'` - -- [ ] **Step 3: Implement constructor + get/set params** - -Create `diff_diff/wooldridge.py`: - -```python -"""WooldridgeDiD: Extended Two-Way Fixed Effects (ETWFE) estimator. - -Implements Wooldridge (2021, 2023) ETWFE, faithful to the Stata jwdid package. - -References ----------- -Wooldridge (2021). Two-Way Fixed Effects, the Two-Way Mundlak Regression, - and Difference-in-Differences Estimators. SSRN 3906345. -Wooldridge (2023). Simple approaches to nonlinear difference-in-differences - with panel data. The Econometrics Journal, 26(3), C31-C66. -Friosavila (2021). jwdid: Stata module. SSC s459114. -""" -from __future__ import annotations - -from typing import Any, Dict, List, Optional, Tuple - -import numpy as np -import pandas as pd - -from diff_diff.linalg import compute_robust_vcov, solve_logit, solve_ols, solve_poisson -from diff_diff.utils import safe_inference, within_transform -from diff_diff.wooldridge_results import WooldridgeDiDResults - -_VALID_METHODS = ("ols", "logit", "poisson") -_VALID_CONTROL_GROUPS = ("never_treated", "not_yet_treated") -_VALID_BOOTSTRAP_WEIGHTS = ("rademacher", "webb", "mammen") - - -class WooldridgeDiD: - """Extended Two-Way Fixed Effects (ETWFE) DiD estimator. - - Implements the Wooldridge (2021) saturated cohort×time regression and - Wooldridge (2023) nonlinear extensions (logit, Poisson). Produces all - four ``jwdid_estat`` aggregation types: simple, group, calendar, event. - - Parameters - ---------- - method : {"ols", "logit", "poisson"} - Estimation method. "ols" for continuous outcomes; "logit" for binary - or fractional outcomes; "poisson" for count data. - control_group : {"not_yet_treated", "never_treated"} - Which units serve as the comparison group. "not_yet_treated" (jwdid - default) uses all untreated observations at each time period; - "never_treated" uses only units never treated throughout the sample. - anticipation : int - Number of periods before treatment onset to include as treatment cells - (anticipation effects). 0 means no anticipation. - demean_covariates : bool - If True (jwdid default), ``xtvar`` covariates are demeaned within each - cohort×period cell before entering the regression. Set to False to - replicate jwdid's ``xasis`` option. - alpha : float - Significance level for confidence intervals. - cluster : str or None - Column name to use for cluster-robust SEs. Defaults to the ``unit`` - identifier passed to ``fit()``. - n_bootstrap : int - Number of bootstrap replications. 0 disables bootstrap. - bootstrap_weights : {"rademacher", "webb", "mammen"} - Bootstrap weight distribution. - seed : int or None - Random seed for reproducibility. - rank_deficient_action : {"warn", "error", "silent"} - How to handle rank-deficient design matrices. - """ - - def __init__( - self, - method: str = "ols", - control_group: str = "not_yet_treated", - anticipation: int = 0, - demean_covariates: bool = True, - alpha: float = 0.05, - cluster: Optional[str] = None, - n_bootstrap: int = 0, - bootstrap_weights: str = "rademacher", - seed: Optional[int] = None, - rank_deficient_action: str = "warn", - ) -> None: - if method not in _VALID_METHODS: - raise ValueError(f"method must be one of {_VALID_METHODS}, got {method!r}") - if control_group not in _VALID_CONTROL_GROUPS: - raise ValueError( - f"control_group must be one of {_VALID_CONTROL_GROUPS}, got {control_group!r}" - ) - if anticipation < 0: - raise ValueError(f"anticipation must be >= 0, got {anticipation}") - - self.method = method - self.control_group = control_group - self.anticipation = anticipation - self.demean_covariates = demean_covariates - self.alpha = alpha - self.cluster = cluster - self.n_bootstrap = n_bootstrap - self.bootstrap_weights = bootstrap_weights - self.seed = seed - self.rank_deficient_action = rank_deficient_action - - self.is_fitted_: bool = False - self._results: Optional[WooldridgeDiDResults] = None - - @property - def results_(self) -> WooldridgeDiDResults: - if not self.is_fitted_: - raise RuntimeError("Call fit() before accessing results_") - return self._results # type: ignore[return-value] - - def get_params(self) -> Dict[str, Any]: - """Return estimator parameters (sklearn-compatible).""" - return { - "method": self.method, - "control_group": self.control_group, - "anticipation": self.anticipation, - "demean_covariates": self.demean_covariates, - "alpha": self.alpha, - "cluster": self.cluster, - "n_bootstrap": self.n_bootstrap, - "bootstrap_weights": self.bootstrap_weights, - "seed": self.seed, - "rank_deficient_action": self.rank_deficient_action, - } - - def set_params(self, **params: Any) -> "WooldridgeDiD": - """Set estimator parameters (sklearn-compatible). Returns self.""" - for key, value in params.items(): - if not hasattr(self, key): - raise ValueError(f"Unknown parameter: {key!r}") - setattr(self, key, value) - return self - - def fit( - self, - data: pd.DataFrame, - outcome: str, - unit: str, - time: str, - cohort: str, - exovar: Optional[List[str]] = None, - xtvar: Optional[List[str]] = None, - xgvar: Optional[List[str]] = None, - ) -> WooldridgeDiDResults: - """Fit the ETWFE model. See class docstring for parameter details. - - Parameters - ---------- - data : DataFrame with panel data (long format) - outcome : outcome column name - unit : unit identifier column - time : time period column - cohort : first treatment period (0 or NaN = never treated) - exovar : time-invariant covariates added without interaction/demeaning - xtvar : time-varying covariates (demeaned within cohort×period cells - when ``demean_covariates=True``) - xgvar : covariates interacted with each cohort indicator - """ - # Placeholder — implementation in Tasks 4 & 5 - raise NotImplementedError("fit() implemented in later tasks") -``` - -- [ ] **Step 4: Run tests to verify they pass** - -```bash -pytest tests/test_wooldridge.py::TestWooldridgeDiDAPI -v -``` - -Expected: all 9 tests PASS - -- [ ] **Step 5: Commit** - -```bash -git add diff_diff/wooldridge.py tests/test_wooldridge.py -git commit -m "feat: add WooldridgeDiD class scaffold with constructor and param API" -``` - ---- - -## Task 4: Data preparation helpers (filter, interaction matrix, covariate prep) - -**Files:** -- Modify: `diff_diff/wooldridge.py` (add private helpers) -- Test: `tests/test_wooldridge.py` (add internal prep tests) - -- [ ] **Step 1: Write the failing tests** - -Add to `tests/test_wooldridge.py`: - -```python -from diff_diff.wooldridge import ( - _filter_sample, - _build_interaction_matrix, - _prepare_covariates, -) - - -def _make_panel(n_units=10, n_periods=5, treat_share=0.5, seed=0): - """Create a simple balanced panel for testing.""" - rng = np.random.default_rng(seed) - units = np.arange(n_units) - n_treated = int(n_units * treat_share) - # Two cohorts: half treated in period 3, rest never treated - cohort = np.array([3] * n_treated + [0] * (n_units - n_treated)) - rows = [] - for u in units: - for t in range(1, n_periods + 1): - rows.append({"unit": u, "time": t, "cohort": cohort[u], - "y": rng.standard_normal(), "x1": rng.standard_normal()}) - return pd.DataFrame(rows) - - -class TestDataPrep: - def test_filter_sample_not_yet_treated(self): - df = _make_panel() - filtered = _filter_sample(df, unit="unit", time="time", cohort="cohort", - control_group="not_yet_treated", anticipation=0) - # All treated units should be present (all periods) - treated_units = df[df["cohort"] == 3]["unit"].unique() - assert set(treated_units).issubset(filtered["unit"].unique()) - - def test_filter_sample_never_treated(self): - df = _make_panel() - filtered = _filter_sample(df, unit="unit", time="time", cohort="cohort", - control_group="never_treated", anticipation=0) - # Only never-treated (cohort==0) and treated units should remain - # No not-yet-treated-only units; here all non-treated have cohort==0 - assert (filtered["cohort"].isin([0, 3])).all() - - def test_build_interaction_matrix_columns(self): - df = _make_panel() - filtered = _filter_sample(df, "unit", "time", "cohort", - "not_yet_treated", anticipation=0) - X_int, col_names, gt_keys = _build_interaction_matrix( - filtered, cohort="cohort", time="time", anticipation=0 - ) - # Each column should be a valid (g, t) pair with t >= g - for (g, t) in gt_keys: - assert t >= g - - def test_build_interaction_matrix_binary(self): - df = _make_panel() - filtered = _filter_sample(df, "unit", "time", "cohort", - "not_yet_treated", anticipation=0) - X_int, col_names, gt_keys = _build_interaction_matrix( - filtered, cohort="cohort", time="time", anticipation=0 - ) - # All values should be 0 or 1 - assert set(np.unique(X_int)).issubset({0, 1}) - - def test_prepare_covariates_exovar(self): - df = _make_panel() - X_cov = _prepare_covariates(df, exovar=["x1"], xtvar=None, xgvar=None, - cohort="cohort", time="time", - demean_covariates=True, groups=[3]) - assert X_cov.shape[0] == len(df) - assert X_cov.shape[1] == 1 # just x1 - - def test_prepare_covariates_xtvar_demeaned(self): - df = _make_panel() - X_raw = _prepare_covariates(df, exovar=None, xtvar=["x1"], xgvar=None, - cohort="cohort", time="time", - demean_covariates=False, groups=[3]) - X_dem = _prepare_covariates(df, exovar=None, xtvar=["x1"], xgvar=None, - cohort="cohort", time="time", - demean_covariates=True, groups=[3]) - # Demeaned version should differ from raw - assert not np.allclose(X_raw, X_dem) -``` - -- [ ] **Step 2: Run tests to verify they fail** - -```bash -pytest tests/test_wooldridge.py::TestDataPrep -v -``` - -Expected: `ImportError` (functions not yet defined) - -- [ ] **Step 3: Implement helper functions** - -Add to `diff_diff/wooldridge.py` (before the `WooldridgeDiD` class definition): - -```python -def _filter_sample( - data: pd.DataFrame, - unit: str, - time: str, - cohort: str, - control_group: str, - anticipation: int, -) -> pd.DataFrame: - """Return the analysis sample following jwdid selection rules. - - Treated units: all observations kept (pre-treatment window beyond - anticipation is not used as a treatment cell but is kept for FE). - Control units: for "not_yet_treated", units with cohort > t at each t - (including never-treated); for "never_treated", only cohort == 0/NaN. - """ - df = data.copy() - # Normalise never-treated: fill NaN cohort with 0 - df[cohort] = df[cohort].fillna(0) - - treated_mask = df[cohort] > 0 - - if control_group == "never_treated": - control_mask = df[cohort] == 0 - else: # not_yet_treated - # A unit is "not yet treated" at time t if its cohort > t - control_mask = (~treated_mask) | (df[cohort] > df[time]) - # Keep untreated-at-t observations for not-yet-treated units - control_mask = (df[cohort] == 0) | (df[cohort] > df[time]) - - return df[treated_mask | control_mask].copy() - - -def _build_interaction_matrix( - data: pd.DataFrame, - cohort: str, - time: str, - anticipation: int, -) -> Tuple[np.ndarray, List[str], List[Tuple[Any, Any]]]: - """Build the saturated cohort×time interaction design matrix. - - Returns - ------- - X_int : (n, n_cells) binary indicator matrix - col_names : list of string labels "g{g}_t{t}" - gt_keys : list of (g, t) tuples in same column order - """ - groups = sorted(g for g in data[cohort].unique() if g > 0) - times = sorted(data[time].unique()) - cohort_vals = data[cohort].values - time_vals = data[time].values - - cols = [] - col_names = [] - gt_keys = [] - - for g in groups: - for t in times: - if t >= g - anticipation: - indicator = ((cohort_vals == g) & (time_vals == t)).astype(float) - cols.append(indicator) - col_names.append(f"g{g}_t{t}") - gt_keys.append((g, t)) - - if not cols: - return np.empty((len(data), 0)), [], [] - return np.column_stack(cols), col_names, gt_keys - - -def _prepare_covariates( - data: pd.DataFrame, - exovar: Optional[List[str]], - xtvar: Optional[List[str]], - xgvar: Optional[List[str]], - cohort: str, - time: str, - demean_covariates: bool, - groups: List[Any], -) -> Optional[np.ndarray]: - """Build covariate matrix following jwdid covariate type conventions. - - Returns None if no covariates, else (n, k) array. - """ - parts = [] - - if exovar: - parts.append(data[exovar].values.astype(float)) - - if xtvar: - if demean_covariates: - # Within-cohort×period demeaning - grp_key = data[cohort].astype(str) + "_" + data[time].astype(str) - tmp = data[xtvar].copy() - for col in xtvar: - tmp[col] = tmp[col] - tmp.groupby(grp_key)[col].transform("mean") - parts.append(tmp.values.astype(float)) - else: - parts.append(data[xtvar].values.astype(float)) - - if xgvar: - for g in groups: - g_indicator = (data[cohort] == g).values.astype(float) - for col in xgvar: - parts.append((g_indicator * data[col].values).reshape(-1, 1)) - - if not parts: - return None - return np.hstack([p if p.ndim == 2 else p.reshape(-1, 1) for p in parts]) -``` - -Also update the imports at the top of the file to expose these as module-level functions -(they are already defined at module level, so they will be importable). - -- [ ] **Step 4: Run tests to verify they pass** - -```bash -pytest tests/test_wooldridge.py::TestDataPrep -v -``` - -Expected: all 6 tests PASS - -- [ ] **Step 5: Commit** - -```bash -git add diff_diff/wooldridge.py tests/test_wooldridge.py -git commit -m "feat: add ETWFE data preparation helpers (filter, interactions, covariates)" -``` - ---- - -## Task 5: Linear ETWFE `fit()` (OLS path) - -**Files:** -- Modify: `diff_diff/wooldridge.py` (implement `fit()` for method="ols") -- Test: `tests/test_wooldridge.py` (add fit tests) - -- [ ] **Step 1: Write the failing tests** - -Add to `tests/test_wooldridge.py`: - -```python -from diff_diff import load_dataset # or: from diff_diff.datasets import load_mpdta - - -class TestWooldridgeDiDFitOLS: - @pytest.fixture - def mpdta(self): - from diff_diff.datasets import load_mpdta - return load_mpdta() - - def test_fit_returns_results(self, mpdta): - est = WooldridgeDiD() - results = est.fit(mpdta, outcome="lemp", unit="countyreal", - time="year", cohort="first.treat") - assert isinstance(results, WooldridgeDiDResults) - - def test_fit_sets_is_fitted(self, mpdta): - est = WooldridgeDiD() - est.fit(mpdta, outcome="lemp", unit="countyreal", - time="year", cohort="first.treat") - assert est.is_fitted_ - - def test_overall_att_finite(self, mpdta): - est = WooldridgeDiD() - r = est.fit(mpdta, outcome="lemp", unit="countyreal", - time="year", cohort="first.treat") - assert np.isfinite(r.overall_att) - assert np.isfinite(r.overall_se) - assert r.overall_se > 0 - - def test_group_time_effects_populated(self, mpdta): - est = WooldridgeDiD() - r = est.fit(mpdta, outcome="lemp", unit="countyreal", - time="year", cohort="first.treat") - assert len(r.group_time_effects) > 0 - for (g, t), eff in r.group_time_effects.items(): - assert t >= g - assert "att" in eff and "se" in eff - - def test_all_inference_fields_finite(self, mpdta): - """No inference field should be NaN in normal data.""" - est = WooldridgeDiD() - r = est.fit(mpdta, outcome="lemp", unit="countyreal", - time="year", cohort="first.treat") - assert np.isfinite(r.overall_t_stat) - assert np.isfinite(r.overall_p_value) - assert all(np.isfinite(c) for c in r.overall_conf_int) - - def test_never_treated_control_group(self, mpdta): - est = WooldridgeDiD(control_group="never_treated") - r = est.fit(mpdta, outcome="lemp", unit="countyreal", - time="year", cohort="first.treat") - assert len(r.group_time_effects) > 0 - - def test_metadata_correct(self, mpdta): - est = WooldridgeDiD() - r = est.fit(mpdta, outcome="lemp", unit="countyreal", - time="year", cohort="first.treat") - assert r.method == "ols" - assert r.n_obs > 0 - assert r.n_treated_units > 0 - assert r.n_control_units > 0 -``` - -- [ ] **Step 2: Run tests to verify they fail** - -```bash -pytest tests/test_wooldridge.py::TestWooldridgeDiDFitOLS -v -``` - -Expected: `NotImplementedError: fit() implemented in later tasks` - -- [ ] **Step 3: Implement `fit()` OLS path** - -Replace the `fit()` placeholder in `diff_diff/wooldridge.py` with the full implementation. -Also add the `_fit_ols`, `_compute_gt_inference`, and `_build_simple_aggregation` helpers: - -```python -def fit( - self, - data: pd.DataFrame, - outcome: str, - unit: str, - time: str, - cohort: str, - exovar: Optional[List[str]] = None, - xtvar: Optional[List[str]] = None, - xgvar: Optional[List[str]] = None, -) -> WooldridgeDiDResults: - """Fit the ETWFE model.""" - df = data.copy() - df[cohort] = df[cohort].fillna(0) - - # 1. Filter to analysis sample - sample = _filter_sample(df, unit, time, cohort, self.control_group, self.anticipation) - - # 2. Build interaction matrix - X_int, col_names, gt_keys = _build_interaction_matrix( - sample, cohort=cohort, time=time, anticipation=self.anticipation - ) - - # 3. Covariates - groups = sorted(g for g in sample[cohort].unique() if g > 0) - X_cov = _prepare_covariates( - sample, exovar=exovar, xtvar=xtvar, xgvar=xgvar, - cohort=cohort, time=time, - demean_covariates=self.demean_covariates, - groups=groups, - ) - - all_regressors = col_names.copy() - if X_cov is not None: - X_design = np.hstack([X_int, X_cov]) - for i in range(X_cov.shape[1]): - all_regressors.append(f"_cov_{i}") - else: - X_design = X_int - - if self.method == "ols": - results = self._fit_ols( - sample, outcome, unit, time, cohort, - X_design, all_regressors, gt_keys, col_names, - groups, exovar, xtvar, xgvar, - ) - elif self.method == "logit": - results = self._fit_logit( - sample, outcome, unit, time, cohort, - X_design, all_regressors, gt_keys, col_names, groups, - ) - else: # poisson - results = self._fit_poisson( - sample, outcome, unit, time, cohort, - X_design, all_regressors, gt_keys, col_names, groups, - ) - - self._results = results - self.is_fitted_ = True - return results - - -def _fit_ols( - self, - sample: pd.DataFrame, - outcome: str, - unit: str, - time: str, - cohort: str, - X_design: np.ndarray, - col_names: List[str], - gt_keys: List[Tuple], - int_col_names: List[str], - groups: List[Any], - exovar, xtvar, xgvar, -) -> WooldridgeDiDResults: - """OLS path: within-transform FE, solve_ols, cluster SE.""" - n_int = len(int_col_names) # number of treatment interaction columns - - # 4. Within-transform: absorb unit + time FE - all_vars = [outcome] + [f"_x{i}" for i in range(X_design.shape[1])] - tmp = sample[[unit, time]].copy() - tmp[outcome] = sample[outcome].values - for i in range(X_design.shape[1]): - tmp[f"_x{i}"] = X_design[:, i] - - transformed = within_transform(tmp, all_vars, unit=unit, time=time, - suffix="_demeaned") - - y = transformed[f"{outcome}_demeaned"].values - X_cols = [f"_x{i}_demeaned" for i in range(X_design.shape[1])] - X = transformed[X_cols].values - - # 5. Cluster IDs (default: unit level) - cluster_col = self.cluster if self.cluster else unit - cluster_ids = sample[cluster_col].values - - # 6. Solve OLS - coefs, resids, vcov = solve_ols( - X, y, - cluster_ids=cluster_ids, - return_vcov=True, - rank_deficient_action=self.rank_deficient_action, - column_names=col_names, - ) - - # 7. Extract β_{g,t} and build gt_effects dict - gt_effects = {} - gt_weights = {} - for idx, (g, t) in enumerate(gt_keys): - if idx >= len(coefs): - break - att = float(coefs[idx]) - se = float(np.sqrt(vcov[idx, idx])) if vcov is not None else float("nan") - t_stat, p_value, conf_int = safe_inference(att, se, alpha=self.alpha) - gt_effects[(g, t)] = { - "att": att, "se": se, - "t_stat": t_stat, "p_value": p_value, "conf_int": conf_int, - } - gt_weights[(g, t)] = int(( - (sample[cohort] == g) & (sample[time] == t) - ).sum()) - - # Extract vcov submatrix for beta_{g,t} only - n_gt = len(gt_keys) - gt_vcov = vcov[:n_gt, :n_gt] if vcov is not None else None - gt_keys_ordered = list(gt_keys) - - # 8. Simple aggregation (always computed) - overall = _compute_weighted_agg(gt_effects, gt_weights, gt_keys_ordered, - gt_vcov, self.alpha) - - # Metadata - n_treated = int(sample[sample[cohort] > 0][unit].nunique()) - n_control = int(sample[sample[cohort] == 0][unit].nunique()) - all_times = sorted(sample[time].unique().tolist()) - - results = WooldridgeDiDResults( - group_time_effects=gt_effects, - overall_att=overall["att"], - overall_se=overall["se"], - overall_t_stat=overall["t_stat"], - overall_p_value=overall["p_value"], - overall_conf_int=overall["conf_int"], - method=self.method, - control_group=self.control_group, - groups=groups, - time_periods=all_times, - n_obs=len(sample), - n_treated_units=n_treated, - n_control_units=n_control, - alpha=self.alpha, - _gt_weights=gt_weights, - _gt_vcov=gt_vcov, - _gt_keys=gt_keys_ordered, - ) - return results - - -def _compute_weighted_agg( - gt_effects: Dict, - gt_weights: Dict, - gt_keys: List, - gt_vcov: Optional[np.ndarray], - alpha: float, -) -> Dict: - """Compute simple (overall) weighted average ATT and SE via delta method.""" - post_keys = [(g, t) for (g, t) in gt_keys if t >= g] - w_total = sum(gt_weights.get(k, 0) for k in post_keys) - if w_total == 0: - att = float("nan") - se = float("nan") - else: - att = sum(gt_weights.get(k, 0) * gt_effects[k]["att"] - for k in post_keys if k in gt_effects) / w_total - if gt_vcov is not None: - w_vec = np.array([ - gt_weights.get(k, 0) / w_total if k in post_keys else 0.0 - for k in gt_keys - ]) - var = float(w_vec @ gt_vcov @ w_vec) - se = float(np.sqrt(max(var, 0.0))) - else: - se = float("nan") - - t_stat, p_value, conf_int = safe_inference(att, se, alpha=alpha) - return {"att": att, "se": se, "t_stat": t_stat, - "p_value": p_value, "conf_int": conf_int} -``` - -Note: add `_fit_logit` and `_fit_poisson` stubs that raise `NotImplementedError` -(will be implemented in Task 7 & 8). - -- [ ] **Step 4: Run tests to verify they pass** - -```bash -pytest tests/test_wooldridge.py::TestWooldridgeDiDFitOLS -v -``` - -Expected: all 7 tests PASS - -- [ ] **Step 5: Commit** - -```bash -git add diff_diff/wooldridge.py tests/test_wooldridge.py -git commit -m "feat: implement WooldridgeDiD.fit() OLS path with ETWFE saturated regression" -``` - ---- - -## Task 6: Aggregation and output methods - -**Files:** -- Test: `tests/test_wooldridge.py` (add aggregation correctness tests) - -- [ ] **Step 1: Write the failing tests** - -Add to `tests/test_wooldridge.py`: - -```python -class TestAggregations: - @pytest.fixture - def fitted(self): - from diff_diff.datasets import load_mpdta - df = load_mpdta() - est = WooldridgeDiD() - return est.fit(df, outcome="lemp", unit="countyreal", - time="year", cohort="first.treat") - - def test_simple_matches_manual_weighted_average(self, fitted): - """simple ATT must equal manually computed weighted average of ATT(g,t).""" - gt = fitted.group_time_effects - w = fitted._gt_weights - post_keys = [(g, t) for (g, t) in w if t >= g] - w_total = sum(w[k] for k in post_keys) - manual_att = sum(w[k] * gt[k]["att"] for k in post_keys) / w_total - assert abs(fitted.overall_att - manual_att) < 1e-10 - - def test_aggregate_group_keys_match_cohorts(self, fitted): - fitted.aggregate("group") - assert set(fitted.group_effects.keys()) == set(fitted.groups) - - def test_aggregate_event_relative_periods(self, fitted): - fitted.aggregate("event") - for k in fitted.event_study_effects: - assert isinstance(k, (int, np.integer)) - - def test_aggregate_calendar_finite(self, fitted): - fitted.aggregate("calendar") - for t, eff in fitted.calendar_effects.items(): - assert np.isfinite(eff["att"]) - - def test_summary_runs(self, fitted): - s = fitted.summary("simple") - assert "ETWFE" in s or "Wooldridge" in s - - def test_to_dataframe_event(self, fitted): - fitted.aggregate("event") - df = fitted.to_dataframe("event") - assert "relative_period" in df.columns - assert "att" in df.columns - - def test_to_dataframe_gt(self, fitted): - df = fitted.to_dataframe("gt") - assert "cohort" in df.columns - assert "time" in df.columns - assert len(df) == len(fitted.group_time_effects) -``` - -- [ ] **Step 2: Run tests to verify they pass (most should already pass)** - -```bash -pytest tests/test_wooldridge.py::TestAggregations -v -``` - -Expected: all 7 tests PASS (aggregation logic is in `WooldridgeDiDResults`) - -- [ ] **Step 3: Commit if any fixes needed** - -If any tests reveal bugs in the aggregation code, fix and then: - -```bash -git add diff_diff/wooldridge_results.py diff_diff/wooldridge.py tests/test_wooldridge.py -git commit -m "fix: aggregation correctness and output method alignment" -``` - ---- - -## Task 7: Nonlinear fit — logit path - -**Files:** -- Modify: `diff_diff/wooldridge.py` (implement `_fit_logit`) -- Test: `tests/test_wooldridge.py` (add logit tests) - -- [ ] **Step 1: Write the failing tests** - -Add to `tests/test_wooldridge.py`: - -```python -class TestWooldridgeDiDLogit: - @pytest.fixture - def binary_panel(self): - """Simulated binary outcome panel with known positive ATT.""" - rng = np.random.default_rng(42) - n_units, n_periods = 60, 5 - rows = [] - for u in range(n_units): - cohort = 3 if u < 30 else 0 - for t in range(1, n_periods + 1): - treated = int(cohort > 0 and t >= cohort) - eta = -0.5 + 1.0 * treated + 0.1 * rng.standard_normal() - y = int(rng.random() < 1 / (1 + np.exp(-eta))) - rows.append({"unit": u, "time": t, "cohort": cohort, "y": y}) - return pd.DataFrame(rows) - - def test_logit_fit_runs(self, binary_panel): - est = WooldridgeDiD(method="logit") - r = est.fit(binary_panel, outcome="y", unit="unit", - time="time", cohort="cohort") - assert isinstance(r, WooldridgeDiDResults) - - def test_logit_att_sign(self, binary_panel): - """ATT should be positive (treatment increases binary outcome).""" - est = WooldridgeDiD(method="logit") - r = est.fit(binary_panel, outcome="y", unit="unit", - time="time", cohort="cohort") - assert r.overall_att > 0 - - def test_logit_se_positive(self, binary_panel): - est = WooldridgeDiD(method="logit") - r = est.fit(binary_panel, outcome="y", unit="unit", - time="time", cohort="cohort") - assert r.overall_se > 0 - - def test_logit_method_stored(self, binary_panel): - est = WooldridgeDiD(method="logit") - r = est.fit(binary_panel, outcome="y", unit="unit", - time="time", cohort="cohort") - assert r.method == "logit" -``` - -- [ ] **Step 2: Run tests to verify they fail** - -```bash -pytest tests/test_wooldridge.py::TestWooldridgeDiDLogit -v -``` - -Expected: `NotImplementedError` from `_fit_logit` stub - -- [ ] **Step 3: Implement `_fit_logit`** - -Add to `diff_diff/wooldridge.py` as a method of `WooldridgeDiD`: - -```python -def _fit_logit( - self, - sample: pd.DataFrame, - outcome: str, - unit: str, - time: str, - cohort: str, - X_int: np.ndarray, - col_names: List[str], - gt_keys: List[Tuple], - int_col_names: List[str], - groups: List[Any], -) -> WooldridgeDiDResults: - """Logit path: cohort×period group FE + solve_logit + ASF ATT.""" - n_int = len(int_col_names) - - # Build cohort×period group FE dummies (drop one to avoid collinearity - # with solve_logit's internal intercept) - grp_label = ( - sample[cohort].astype(str) + "_" + sample[time].astype(str) - ) - group_dummies = pd.get_dummies(grp_label, drop_first=True).values.astype(float) - - # Design matrix: treatment interactions + group FE dummies - X_full = np.hstack([X_int, group_dummies]) - - y = sample[outcome].values.astype(float) - cluster_col = self.cluster if self.cluster else unit - cluster_ids = sample[cluster_col].values - - beta, probs = solve_logit( - X_full, y, - rank_deficient_action=self.rank_deficient_action, - ) - # solve_logit prepends intercept — beta[0] is intercept, beta[1:] are X_full cols - beta_int_cols = beta[1: n_int + 1] # treatment interaction coefficients - - # Sandwich vcov for X_full (excluding intercept position 0) - resids = y - probs - X_with_intercept = np.column_stack([np.ones(len(y)), X_full]) - vcov_full = compute_robust_vcov( - X_with_intercept, resids, cluster_ids=cluster_ids, - weights=probs * (1 - probs), # logit variance weights - ) - # Submatrix for treatment interactions (skip intercept col 0) - vcov_int = vcov_full[1: n_int + 1, 1: n_int + 1] - - # ASF ATT(g,t) for treated units in each cell - gt_effects = {} - gt_weights = {} - for idx, (g, t) in enumerate(gt_keys): - if idx >= n_int: - break - cell_mask = (sample[cohort] == g) & (sample[time] == t) - if cell_mask.sum() == 0: - continue - # Baseline linear index for treated units in this cell - eta_base = X_with_intercept[cell_mask] @ beta # includes intercept - att = float(np.mean( - _logistic(eta_base + beta_int_cols[idx]) - _logistic(eta_base) - )) - # Delta method: full gradient over all K parameters (including intercept) - d_delta = np.mean( - _logistic_deriv(eta_base + beta_int_cols[idx]) - ) - d_base = X_with_intercept[cell_mask] * ( - _logistic_deriv(eta_base + beta_int_cols[idx]) - - _logistic_deriv(eta_base) - )[:, None] - grad = np.zeros(len(beta)) - grad[1 + idx] = d_delta - grad[1: n_int + 1] += np.zeros(n_int) # other deltas don't contribute - # base coefficient gradients - grad += np.mean(d_base, axis=0) - se = float(np.sqrt(max(grad @ vcov_full @ grad, 0.0))) - t_stat, p_value, conf_int = safe_inference(att, se, alpha=self.alpha) - gt_effects[(g, t)] = { - "att": att, "se": se, - "t_stat": t_stat, "p_value": p_value, "conf_int": conf_int, - } - gt_weights[(g, t)] = int(cell_mask.sum()) - - gt_keys_ordered = [k for k in gt_keys if k in gt_effects] - gt_vcov = None # full delta method used per-cell; aggregation uses None fallback - - overall = _compute_weighted_agg(gt_effects, gt_weights, gt_keys_ordered, - gt_vcov, self.alpha) - - return WooldridgeDiDResults( - group_time_effects=gt_effects, - overall_att=overall["att"], - overall_se=overall["se"], - overall_t_stat=overall["t_stat"], - overall_p_value=overall["p_value"], - overall_conf_int=overall["conf_int"], - method=self.method, - control_group=self.control_group, - groups=groups, - time_periods=sorted(sample[time].unique().tolist()), - n_obs=len(sample), - n_treated_units=int(sample[sample[cohort] > 0][unit].nunique()), - n_control_units=int(sample[sample[cohort] == 0][unit].nunique()), - alpha=self.alpha, - _gt_weights=gt_weights, - _gt_vcov=gt_vcov, - _gt_keys=gt_keys_ordered, - ) -``` - -Add helper functions at module level (before the class): - -```python -def _logistic(x: np.ndarray) -> np.ndarray: - return 1.0 / (1.0 + np.exp(-x)) - - -def _logistic_deriv(x: np.ndarray) -> np.ndarray: - p = _logistic(x) - return p * (1.0 - p) -``` - -- [ ] **Step 4: Run tests** - -```bash -pytest tests/test_wooldridge.py::TestWooldridgeDiDLogit -v -``` - -Expected: all 4 tests PASS - -- [ ] **Step 5: Commit** - -```bash -git add diff_diff/wooldridge.py tests/test_wooldridge.py -git commit -m "feat: implement WooldridgeDiD logit path with ASF ATT and delta-method SEs" -``` - ---- - -## Task 8: Nonlinear fit — Poisson path - -**Files:** -- Modify: `diff_diff/wooldridge.py` (implement `_fit_poisson`) -- Test: `tests/test_wooldridge.py` (add Poisson tests) - -- [ ] **Step 1: Write the failing tests** - -Add to `tests/test_wooldridge.py`: - -```python -class TestWooldridgeDiDPoisson: - @pytest.fixture - def count_panel(self): - rng = np.random.default_rng(7) - n_units, n_periods = 60, 5 - rows = [] - for u in range(n_units): - cohort = 3 if u < 30 else 0 - for t in range(1, n_periods + 1): - treated = int(cohort > 0 and t >= cohort) - mu = np.exp(0.5 + 0.8 * treated + 0.1 * rng.standard_normal()) - y = rng.poisson(mu) - rows.append({"unit": u, "time": t, "cohort": cohort, "y": float(y)}) - return pd.DataFrame(rows) - - def test_poisson_fit_runs(self, count_panel): - est = WooldridgeDiD(method="poisson") - r = est.fit(count_panel, outcome="y", unit="unit", - time="time", cohort="cohort") - assert isinstance(r, WooldridgeDiDResults) - - def test_poisson_att_sign(self, count_panel): - est = WooldridgeDiD(method="poisson") - r = est.fit(count_panel, outcome="y", unit="unit", - time="time", cohort="cohort") - assert r.overall_att > 0 - - def test_poisson_se_positive(self, count_panel): - est = WooldridgeDiD(method="poisson") - r = est.fit(count_panel, outcome="y", unit="unit", - time="time", cohort="cohort") - assert r.overall_se > 0 -``` - -- [ ] **Step 2: Run tests to verify they fail** - -```bash -pytest tests/test_wooldridge.py::TestWooldridgeDiDPoisson -v -``` - -Expected: `NotImplementedError` - -- [ ] **Step 3: Implement `_fit_poisson`** - -Add to `WooldridgeDiD` (mirrors `_fit_logit` but uses `solve_poisson` and exp link): - -```python -def _fit_poisson( - self, - sample: pd.DataFrame, - outcome: str, - unit: str, - time: str, - cohort: str, - X_int: np.ndarray, - col_names: List[str], - gt_keys: List[Tuple], - int_col_names: List[str], - groups: List[Any], -) -> WooldridgeDiDResults: - """Poisson path: cohort×period group FE + solve_poisson + ASF ATT.""" - n_int = len(int_col_names) - - # Group FE dummies (drop one reference category) - grp_label = ( - sample[cohort].astype(str) + "_" + sample[time].astype(str) - ) - group_dummies = pd.get_dummies(grp_label, drop_first=True).values.astype(float) - - # Design matrix: group FE dummies + treatment interactions - # Poisson solver does NOT prepend intercept; include group FE as baseline - X_full = np.hstack([group_dummies, X_int]) - n_fe = group_dummies.shape[1] - - y = sample[outcome].values.astype(float) - cluster_col = self.cluster if self.cluster else unit - cluster_ids = sample[cluster_col].values - - beta, mu_hat = solve_poisson(X_full, y) - - # Sandwich vcov: (X'WX)^{-1} (X'diag(resid^2)X) (X'WX)^{-1} - resids = y - mu_hat - W = mu_hat # Poisson variance = mean - XtWX = X_full.T @ (W[:, None] * X_full) - try: - XtWX_inv = np.linalg.inv(XtWX) - except np.linalg.LinAlgError: - XtWX_inv = np.full_like(XtWX, float("nan")) - - # Cluster-robust meat - if cluster_ids is not None: - clusters = np.unique(cluster_ids) - meat = np.zeros_like(XtWX) - for c in clusters: - mask = cluster_ids == c - scores_c = (X_full[mask] * resids[mask, None]).sum(axis=0) - meat += np.outer(scores_c, scores_c) - else: - scores = X_full * resids[:, None] - meat = scores.T @ scores - - vcov_full = XtWX_inv @ meat @ XtWX_inv - - # Interaction columns start at column n_fe in X_full - beta_int = beta[n_fe: n_fe + n_int] - vcov_int = vcov_full[n_fe: n_fe + n_int, n_fe: n_fe + n_int] - - # ASF ATT(g,t): E[exp(η + δ) - exp(η)] for treated units in cell - gt_effects = {} - gt_weights = {} - for idx, (g, t) in enumerate(gt_keys): - if idx >= n_int: - break - cell_mask = (sample[cohort] == g) & (sample[time] == t) - if cell_mask.sum() == 0: - continue - eta_base = X_full[cell_mask] @ beta - delta = beta_int[idx] - att = float(np.mean(np.exp(eta_base + delta) - np.exp(eta_base))) - # Delta method gradient - grad_delta = float(np.mean(np.exp(eta_base + delta))) - grad_base = np.mean( - X_full[cell_mask] * ( - np.exp(eta_base + delta) - np.exp(eta_base) - )[:, None], - axis=0, - ) - grad = np.zeros(len(beta)) - grad[n_fe + idx] = grad_delta - grad += grad_base - se = float(np.sqrt(max(grad @ vcov_full @ grad, 0.0))) - t_stat, p_value, conf_int = safe_inference(att, se, alpha=self.alpha) - gt_effects[(g, t)] = { - "att": att, "se": se, - "t_stat": t_stat, "p_value": p_value, "conf_int": conf_int, - } - gt_weights[(g, t)] = int(cell_mask.sum()) - - gt_keys_ordered = [k for k in gt_keys if k in gt_effects] - overall = _compute_weighted_agg(gt_effects, gt_weights, gt_keys_ordered, - None, self.alpha) - - return WooldridgeDiDResults( - group_time_effects=gt_effects, - overall_att=overall["att"], - overall_se=overall["se"], - overall_t_stat=overall["t_stat"], - overall_p_value=overall["p_value"], - overall_conf_int=overall["conf_int"], - method=self.method, - control_group=self.control_group, - groups=groups, - time_periods=sorted(sample[time].unique().tolist()), - n_obs=len(sample), - n_treated_units=int(sample[sample[cohort] > 0][unit].nunique()), - n_control_units=int(sample[sample[cohort] == 0][unit].nunique()), - alpha=self.alpha, - _gt_weights=gt_weights, - _gt_vcov=None, - _gt_keys=gt_keys_ordered, - ) -``` - -- [ ] **Step 4: Run tests** - -```bash -pytest tests/test_wooldridge.py::TestWooldridgeDiDPoisson -v -``` - -Expected: all 3 tests PASS - -- [ ] **Step 5: Commit** - -```bash -git add diff_diff/wooldridge.py tests/test_wooldridge.py -git commit -m "feat: implement WooldridgeDiD Poisson path with ASF ATT" -``` - ---- - -## Task 9: Bootstrap support - -**Files:** -- Modify: `diff_diff/wooldridge.py` (add bootstrap to `_fit_ols`) -- Test: `tests/test_wooldridge.py` (bootstrap test, marked slow) - -- [ ] **Step 1: Write the failing test** - -Add to `tests/test_wooldridge.py`: - -```python -class TestBootstrap: - @pytest.mark.slow - def test_multiplier_bootstrap_ols(self, ci_params): - """Bootstrap SE should be close to analytic SE.""" - from diff_diff.datasets import load_mpdta - df = load_mpdta() - n_boot = ci_params.bootstrap(50, min_n=19) - est = WooldridgeDiD(n_bootstrap=n_boot, seed=42) - r = est.fit(df, outcome="lemp", unit="countyreal", - time="year", cohort="first.treat") - threshold = 0.40 if n_boot < 100 else 0.15 - assert abs(r.overall_se - r.overall_att) / max(abs(r.overall_att), 1e-8) < 10 - # Bootstrap SE should be in same ballpark as analytic SE - # (exact convergence tested with large n_boot) - - def test_bootstrap_zero_disables(self): - from diff_diff.datasets import load_mpdta - df = load_mpdta() - est = WooldridgeDiD(n_bootstrap=0) - r = est.fit(df, outcome="lemp", unit="countyreal", - time="year", cohort="first.treat") - assert np.isfinite(r.overall_se) -``` - -- [ ] **Step 2: Run non-slow tests to verify they pass** - -```bash -pytest tests/test_wooldridge.py::TestBootstrap::test_bootstrap_zero_disables -v -``` - -Expected: PASS (bootstrap=0 path already works) - -- [ ] **Step 3: Implement multiplier bootstrap in `_fit_ols`** - -After the OLS solve in `_fit_ols`, add bootstrap block: - -```python -if self.n_bootstrap > 0: - rng = np.random.default_rng(self.seed) - units_arr = sample[unit].values - unique_units = np.unique(units_arr) - n_clusters = len(unique_units) - boot_atts = [] - for _ in range(self.n_bootstrap): - if self.bootstrap_weights == "rademacher": - unit_weights = rng.choice([-1.0, 1.0], size=n_clusters) - elif self.bootstrap_weights == "webb": - unit_weights = rng.choice( - [-np.sqrt(1.5), -1.0, -np.sqrt(0.5), - np.sqrt(0.5), 1.0, np.sqrt(1.5)], - size=n_clusters, - ) - else: # mammen - phi = (1 + np.sqrt(5)) / 2 - unit_weights = rng.choice( - [-(phi - 1), phi], - p=[phi / np.sqrt(5), (phi - 1) / np.sqrt(5)], - size=n_clusters, - ) - obs_weights = unit_weights[ - np.searchsorted(unique_units, units_arr) - ] - y_boot = y + obs_weights * resids # multiplier perturbation - coefs_b, _, _ = solve_ols( - X, y_boot, - cluster_ids=cluster_ids, - return_vcov=True, - rank_deficient_action="silent", - ) - w_total = sum(gt_weights.get(k, 0) for k in post_keys) - if w_total > 0: - att_b = sum( - gt_weights.get(k, 0) * float(coefs_b[i]) - for i, k in enumerate(gt_keys) if k in post_keys - ) / w_total - boot_atts.append(att_b) - if boot_atts: - # Override SE with bootstrap SE - boot_se = float(np.std(boot_atts, ddof=1)) - overall_att = overall["att"] - t_stat_b, p_b, ci_b = safe_inference(overall_att, boot_se, alpha=self.alpha) - results.overall_se = boot_se - results.overall_t_stat = t_stat_b - results.overall_p_value = p_b - results.overall_conf_int = ci_b -``` - -- [ ] **Step 4: Run all non-slow tests** - -```bash -pytest tests/test_wooldridge.py -v -m "not slow" -``` - -Expected: all PASS - -- [ ] **Step 5: Commit** - -```bash -git add diff_diff/wooldridge.py tests/test_wooldridge.py -git commit -m "feat: add multiplier bootstrap to WooldridgeDiD OLS path" -``` - ---- - -## Task 10: Methodology correctness test (CS equivalence) - -**Files:** -- Test: `tests/test_wooldridge.py` (add parity test) - -- [ ] **Step 1: Write the failing test** - -Add to `tests/test_wooldridge.py`: - -```python -class TestMethodologyCorrectness: - def test_ols_att_sign_direction(self): - """ATT sign should be consistent across cohorts on mpdta.""" - from diff_diff.datasets import load_mpdta - df = load_mpdta() - est = WooldridgeDiD(control_group="never_treated") - r = est.fit(df, outcome="lemp", unit="countyreal", - time="year", cohort="first.treat") - # mpdta ATT is expected to be negative (employment effect of min wage) - # This is a directional check, not exact - assert np.isfinite(r.overall_att) - - def test_never_treated_pre_periods_estimable(self): - """With never_treated control, k < 0 event periods should appear.""" - from diff_diff.datasets import load_mpdta - df = load_mpdta() - est = WooldridgeDiD(control_group="never_treated") - r = est.fit(df, outcome="lemp", unit="countyreal", - time="year", cohort="first.treat") - r.aggregate("event") - relative_periods = list(r.event_study_effects.keys()) - assert any(k < 0 for k in relative_periods), ( - "Expected pre-treatment periods with never_treated control" - ) - - def test_single_cohort_degenerates_to_simple_did(self): - """With one cohort, ETWFE should collapse to a standard DiD.""" - rng = np.random.default_rng(0) - n = 100 - rows = [] - for u in range(n): - cohort = 2 if u < 50 else 0 - for t in [1, 2]: - treated = int(cohort > 0 and t >= cohort) - y = 1.0 * treated + rng.standard_normal() - rows.append({"unit": u, "time": t, "cohort": cohort, "y": y}) - df = pd.DataFrame(rows) - r = WooldridgeDiD().fit(df, outcome="y", unit="unit", - time="time", cohort="cohort") - # One cohort, one post period → one ATT(g=2, t=2) - assert len(r.group_time_effects) == 1 - assert abs(r.overall_att - 1.0) < 0.5 # close to true ATT=1 - - def test_aggregation_weights_sum_to_one(self): - """Simple aggregation weights should sum to 1.""" - from diff_diff.datasets import load_mpdta - df = load_mpdta() - r = WooldridgeDiD().fit(df, outcome="lemp", unit="countyreal", - time="year", cohort="first.treat") - w = r._gt_weights - post_keys = [(g, t) for (g, t) in w if t >= g] - w_total = sum(w[k] for k in post_keys) - norm_weights = [w[k] / w_total for k in post_keys] - assert abs(sum(norm_weights) - 1.0) < 1e-10 -``` - -- [ ] **Step 2: Run tests** - -```bash -pytest tests/test_wooldridge.py::TestMethodologyCorrectness -v -``` - -Expected: all 4 tests PASS - -- [ ] **Step 3: Commit** - -```bash -git add tests/test_wooldridge.py -git commit -m "test: add methodology correctness tests for WooldridgeDiD" -``` - ---- - -## Task 11: Exports, `__init__.py`, and REGISTRY.md - -**Files:** -- Modify: `diff_diff/__init__.py` -- Modify: `docs/methodology/REGISTRY.md` - -- [ ] **Step 1: Write the failing import test** - -Add to `tests/test_wooldridge.py`: - -```python -class TestExports: - def test_top_level_import(self): - from diff_diff import WooldridgeDiD, WooldridgeDiDResults, ETWFE - assert ETWFE is WooldridgeDiD - - def test_alias_etwfe(self): - import diff_diff - assert hasattr(diff_diff, "ETWFE") - assert diff_diff.ETWFE is diff_diff.WooldridgeDiD -``` - -- [ ] **Step 2: Run to verify failure** - -```bash -pytest tests/test_wooldridge.py::TestExports -v -``` - -Expected: `ImportError: cannot import name 'WooldridgeDiD' from 'diff_diff'` - -- [ ] **Step 3: Update `diff_diff/__init__.py`** - -Find the block where other staggered estimators are imported (e.g., near the -`CallawaySantAnna` imports) and add: - -```python -from diff_diff.wooldridge import WooldridgeDiD -from diff_diff.wooldridge_results import WooldridgeDiDResults - -ETWFE = WooldridgeDiD -``` - -Also add to `__all__`: - -```python -"WooldridgeDiD", -"WooldridgeDiDResults", -"ETWFE", -``` - -- [ ] **Step 4: Run export tests** - -```bash -pytest tests/test_wooldridge.py::TestExports -v -``` - -Expected: PASS - -- [ ] **Step 5: Add REGISTRY.md section** - -Open `docs/methodology/REGISTRY.md` and add a new section following the existing -estimator format. Place it after the "StackedDiD" section: - -```markdown -## WooldridgeDiD / ETWFE - -**Primary sources:** -- Wooldridge (2021). "Two-Way Fixed Effects, the Two-Way Mundlak Regression, and - Difference-in-Differences Estimators." SSRN 3906345. -- Wooldridge (2023). "Simple approaches to nonlinear difference-in-differences with - panel data." *The Econometrics Journal*, 26(3), C31–C66. -- Friosavila (2021). `jwdid`: Stata module. SSC s459114. - -**Estimator equation (linear):** - -``` -Y_it = α_i + λ_t + Σ_{g,t: t≥g-a} β_{g,t} · 1(G_i=g) · 1(T=t) + X_it'γ + ε_it -``` - -Where `a` is the anticipation window. Unit and time FE are absorbed via -within-transformation. - -**ATT(g,t) = β_{g,t}** (directly from the regression). - -**Nonlinear models:** For `method="logit"` or `"poisson"`, ATT(g,t) is computed via the -Average Structural Function (ASF): -``` -ATT(g,t) = mean[ g(η_i + δ_{g,t}) - g(η_i) ] over treated units in (g,t) -``` -where `g(·)` is logistic or exp. SEs via full delta method. - -**Standard errors:** -- Default: cluster-robust at `unit` level (matches `jwdid` default `vce(cluster ivar)`) -- Optional: multiplier bootstrap (all methods); wild cluster bootstrap (OLS only) - -**Aggregations (corresponding to `jwdid_estat`):** -- `simple`: overall weighted ATT -- `group`: by treatment cohort -- `calendar`: by calendar period -- `event`: by relative period (event study) - -**Covariate types (corresponding to `jwdid` options):** -- `exovar`: time-invariant; no demeaning (→ `exovar()`) -- `xtvar`: time-varying; demeaned within cohort×period (→ `xtvar()`); raw when - `demean_covariates=False` (→ `xasis`) -- `xgvar`: interacted with cohort indicators (→ `xgvar()`) - -**Nonlinear FE:** Both logit and Poisson use **cohort×period group fixed effects** -(not individual FE) to avoid the incidental parameters problem (Wooldridge 2023). -One dummy category is dropped to avoid collinearity with the implicit constant in -`solve_logit` / intercept column in `solve_poisson`. - -**Edge cases:** -- Single cohort: reduces to standard DiD (one β_{g,t} per post period). -- `never_treated` control: pre-treatment cells (k < 0) are estimable. -- `not_yet_treated` control: pre-treatment cells excluded from model by design. -- `anticipation > 0`: treatment cells shifted left by `anticipation` periods. - -**Reference implementation:** Stata `jwdid` (Friosavila 2021, SSC s459114). - -- **Note:** nonlinear bootstrap uses multiplier bootstrap; jwdid uses delta method. -- **Note:** nonlinear aggregation SEs fall back to NaN when full β vcov unavailable - across cells (delta-method is computed per-cell only). -``` - -- [ ] **Step 6: Run full test suite** - -```bash -pytest tests/test_wooldridge.py -v -m "not slow" -``` - -Expected: all tests PASS - -- [ ] **Step 7: Commit** - -```bash -git add diff_diff/__init__.py docs/methodology/REGISTRY.md tests/test_wooldridge.py -git commit -m "feat: export WooldridgeDiD and ETWFE alias; add REGISTRY.md entry" -``` - ---- - -## Task 12: Final integration and cleanup - -**Files:** -- Run: full test suite to verify no regressions - -- [ ] **Step 1: Run full project test suite** - -```bash -pytest --tb=short -q -``` - -Expected: no new failures vs. baseline (existing tests unaffected) - -- [ ] **Step 2: Verify linting** - -```bash -ruff check diff_diff/wooldridge.py diff_diff/wooldridge_results.py diff_diff/linalg.py -black --check diff_diff/wooldridge.py diff_diff/wooldridge_results.py -``` - -Fix any issues, then re-run. - -- [ ] **Step 3: Smoke-test example** - -```python -from diff_diff import WooldridgeDiD, ETWFE -from diff_diff.datasets import load_mpdta - -df = load_mpdta() -r = WooldridgeDiD().fit(df, outcome="lemp", unit="countyreal", - time="year", cohort="first.treat") -r.aggregate("event") -print(r) -print(r.summary("event")) -``` - -Expected: prints results without error. - -- [ ] **Step 4: Final commit** - -```bash -git add -u -git commit -m "feat: complete WooldridgeDiD (ETWFE) estimator implementation" -``` diff --git a/docs/superpowers/specs/2026-03-18-wooldridge-did-design.md b/docs/superpowers/specs/2026-03-18-wooldridge-did-design.md deleted file mode 100644 index 6b60176b..00000000 --- a/docs/superpowers/specs/2026-03-18-wooldridge-did-design.md +++ /dev/null @@ -1,307 +0,0 @@ -# WooldridgeDiD Estimator — Design Spec - -**Date:** 2026-03-18 -**Status:** Approved -**Scope:** Integrate Stata `jwdid` (Wooldridge ETWFE) functionality into diff-diff - ---- - -## 1. Background and Motivation - -The Stata package `jwdid` (Friosavila 2021) implements Wooldridge's (2021, 2023) Extended -Two-Way Fixed Effects (ETWFE) estimator for staggered DiD. Its key advantages over existing -diff-diff estimators are: - -- **Saturated regression**: estimates all cohort×time ATT(g,t) in a single pooled OLS, - more efficient than Callaway-Sant'Anna's pair-wise approach -- **Nonlinear extension**: Wooldridge (2023) extends ETWFE to logit and Poisson, avoiding - the incidental parameters problem — no other estimator in diff-diff supports this -- **Equivalence to CS**: under identical assumptions, ETWFE ATT(g,t) equals CS ATT(g,t) - -**Primary references:** -- Wooldridge (2021). "Two-Way Fixed Effects, the Two-Way Mundlak Regression, and - Difference-in-Differences Estimators." SSRN 3906345. -- Wooldridge (2023). "Simple approaches to nonlinear difference-in-differences with panel - data." *The Econometrics Journal*, 26(3), C31–C66. -- Friosavila (2021). `jwdid`: Stata module. SSC s459114. - ---- - -## 2. Architecture Overview - -### New files -| File | Purpose | -|------|---------| -| `diff_diff/wooldridge.py` | `WooldridgeDiD` estimator class | -| `diff_diff/wooldridge_results.py` | `WooldridgeDiDResults` dataclass | -| `tests/test_wooldridge.py` | Full test suite | - -### Modified files -| File | Change | -|------|--------| -| `diff_diff/__init__.py` | Export `WooldridgeDiD`, `WooldridgeDiDResults`; alias `ETWFE = WooldridgeDiD` | -| `docs/methodology/REGISTRY.md` | Add ETWFE methodology section | - -### Class hierarchy -`WooldridgeDiD` is a **standalone estimator** (same level as `CallawaySantAnna`, -`SunAbraham`, etc.), not inheriting from `DifferenceInDifferences`. It implements its own -`get_params` / `set_params`. - ---- - -## 3. Public API - -### Constructor - -```python -class WooldridgeDiD: - def __init__( - self, - method: str = "ols", # "ols" | "logit" | "poisson" - control_group: str = "not_yet_treated", # "never_treated" | "not_yet_treated" - anticipation: int = 0, # pre-treatment periods to include - demean_covariates: bool = True, # within cohort-period demeaning (jwdid default) - alpha: float = 0.05, - cluster: Optional[str] = None, # default: unit identifier (jwdid default) - n_bootstrap: int = 0, # >0 enables multiplier bootstrap - bootstrap_weights: str = "rademacher", # "rademacher" | "webb" | "mammen" - seed: Optional[int] = None, - rank_deficient_action: str = "warn", # "warn" | "error" | "silent" - ): ... -``` - -### fit() - -```python -def fit( - self, - data: pd.DataFrame, - outcome: str, - unit: str, - time: str, - cohort: str, # first treatment period; 0/NaN = never treated - exovar: Optional[List[str]] = None, # time-invariant covariates (no interaction) - xtvar: Optional[List[str]] = None, # time-varying covariates (demeaned within cohort-period) - xgvar: Optional[List[str]] = None, # cohort-interacted covariates -) -> "WooldridgeDiDResults": ... -``` - -**Notes:** -- `cohort` column convention: integer = first treatment period, 0 or NaN = never treated. - Consistent with `CallawaySantAnna`'s `cohort` parameter. -- Default clustering is at the `unit` level (matches `jwdid` default of `vce(cluster ivar)`). -- `demean_covariates=True` corresponds to `jwdid` default; `False` corresponds to `xasis` option. -- When `demean_covariates=False`, `xtvar` variables are treated identically to `exovar` - (appended without demeaning or interaction). - -### get_params / set_params - -```python -def get_params(self) -> Dict[str, Any]: ... # returns all constructor params -def set_params(self, **params) -> "WooldridgeDiD": ... # sklearn-compatible -``` - ---- - -## 4. Results Object - -```python -@dataclass -class WooldridgeDiDResults: - # Raw cohort×time estimates — core output - group_time_effects: Dict[Tuple[Any, Any], Dict[str, Any]] - # key = (g, t); value = {"att", "se", "t_stat", "p_value", "conf_int"} - - # Simple aggregation (always computed on fit) - overall_att: float - overall_se: float - overall_t_stat: float - overall_p_value: float - overall_conf_int: Tuple[float, float] - - # Other aggregations (populated by .aggregate()) - group_effects: Optional[Dict[Any, Dict]] # keyed by cohort g - calendar_effects: Optional[Dict[Any, Dict]] # keyed by calendar period t - event_study_effects: Optional[Dict[int, Dict]] # keyed by relative period k = t - g - - # Metadata - method: str - control_group: str - groups: List[Any] - time_periods: List[Any] - n_obs: int - n_treated_units: int - n_control_units: int - alpha: float = 0.05 - - # Methods - def aggregate(self, type: str) -> "WooldridgeDiDResults": ... - # type: "simple" | "group" | "calendar" | "event" - # fills corresponding fields, returns self for chaining - - def summary(self, aggregation: str = "simple") -> str: ... - def to_dataframe(self, aggregation: str = "event") -> pd.DataFrame: ... - def plot_event_study(self, **kwargs) -> None: ... - # delegates to diff_diff.visualization plot utilities (same pattern as CallawaySantAnna) - def __repr__(self) -> str: ... -``` - -**Inference rule:** ALL inference fields (t_stat, p_value, conf_int) computed together -via `safe_inference()` from `diff_diff.utils`. Never computed individually. - ---- - -## 5. Internal Computation - -### 5a. Linear ETWFE (`method="ols"`) - -Faithful port of `jwdid` + `reghdfe`: - -1. **Filter observations**: - - Treated units: keep all observations. - - `not_yet_treated` control: at each time t, include units with cohort > t (not yet - treated). Drop treated units' observations where `t < g - anticipation` (pre-treatment - beyond anticipation window). Control observations are kept for all t. - - `never_treated` control: include only units with cohort = 0 or NaN. Drop treated - unit observations where `t < g - anticipation`. - -2. **Build interaction matrix**: for each (g, t) with `t >= g - anticipation`, create - column `1(G_i = g) * 1(T = t)`. These are the β_{g,t} regressors. Cells with - `t < g - anticipation` are excluded from the model (not constrained — simply absent). - -3. **Covariate preparation**: - - `exovar`: append as-is (no interaction, no demeaning) - - `xtvar`: when `demean_covariates=True`, demean within (cohort × period) cells using - pandas `groupby([cohort_col, time]).transform("mean")`; when `demean_covariates=False`, - append as-is (equivalent to `xasis` option in jwdid) - - `xgvar`: interact each variable with each cohort indicator column - -4. **Absorb unit + time FE**: two-way within-transformation using - `within_transform(data, variables, unit, time, suffix="_demeaned")` from `diff_diff.utils`. - This performs iterative demeaning equivalent to unit + time dummy absorption. Applied to - outcome and all regressors before `solve_ols`. Track demeaned columns via the `_demeaned` - suffix (e.g., `outcome` → `outcome_demeaned`) when slicing the DataFrame for `solve_ols`. - -5. **Solve**: `linalg.solve_ols()` on the within-transformed design matrix → extract - β_{g,t} coefficients and full vcov matrix. - -6. **Inference**: `linalg.compute_robust_vcov()` with unit-level clustering by default - (pass `cluster=unit` column), then `safe_inference()` for each (g, t) cell. - -7. **Bootstrap**: multiplier bootstrap supported for all inference; - wild cluster bootstrap supported for linear only (same as `DifferenceInDifferences`). - -### 5b. Nonlinear (`method="logit"|"poisson"`) - -Following Wooldridge (2023) pooled QMLE approach. Both methods use **explicit cohort×period -group FE** (not individual FE) to avoid the incidental parameters problem: - -- **Logit**: Bernoulli QLL. Use existing `linalg.solve_logit()` (IRLS). Build design matrix - with explicit cohort×period group FE dummies using `pd.get_dummies(..., drop_first=False)`, - then **drop one dummy column** (reference category) before passing to `solve_logit`. - `solve_logit` prepends its own intercept internally; providing dummies that span the - constant without dropping one causes rank deficiency and silent coefficient dropping. - Dropping one cohort×period category is the correct fix (standard dummy variable trap). - -- **Poisson**: Poisson QLL. Implement `linalg.solve_poisson()` — a new function with - signature `solve_poisson(X, y, max_iter=25, tol=1e-8) -> Tuple[ndarray, ndarray]` - (mirrors `solve_logit` signature). Uses Poisson IRLS (Newton-Raphson with log link): - - Initialize: `β = zeros`; `μ̂ = clip(exp(Xβ), 1e-10, None)` (clip prevents log(0)) - - Weight update: `W = diag(μ̂)` - - Newton step: `β ← β + (X'WX)^{-1} X'(y - μ̂)` (score / Hessian) - - Convergence: `‖β_new - β_old‖_∞ < tol`; warn if `max_iter` reached without convergence - - Returns `(β, W_final)` where `W_final` is used by caller for vcov - - Does **not** prepend intercept automatically (caller includes intercept or FE dummies) - - Also uses explicit cohort×period group FE dummies with one dropped (same as logit) - -Vcov for both: sandwich estimator `(X'WX)^{-1} (X'Ûû'X) (X'WX)^{-1}` (robust/clustered), -where Û contains Pearson residuals `(y - μ̂)`. Mirrors `linalg.compute_robust_vcov` pattern. - -**ATT computation via Average Structural Function (ASF):** -Coefficients on treatment interactions are not directly ATTs. Compute: -``` -ATT(g,t) = mean[ g(η_i + δ̂_{g,t}) - g(η_i) ] over treated units in (g,t) -``` -where `η_i = X_i'β̂` is the baseline linear index and `g(·)` = logistic or exp. - -SE via full delta method. Define gradient vector `∇θ ∈ ℝ^K` (one entry per coefficient): -- For `δ_{g,t}`: `∂ATT/∂δ_{g,t} = mean[ g'(η_i + δ̂_{g,t}) ]` -- For any baseline covariate `β_k`: `∂ATT/∂β_k = mean[ x_{ik} (g'(η_i + δ̂_{g,t}) - g'(η_i)) ]` -Then `Var(ATT(g,t)) = ∇θ' Σ_β ∇θ` using the full parameter vcov matrix. - -Bootstrap: multiplier bootstrap only (no wild cluster bootstrap for nonlinear). - -### 5c. Aggregation Weights (exact jwdid_estat formula) - -``` -ω(g,t) = number of unit-time observations in cell (g,t) - -simple: Σ_{g,t: t≥g} ω(g,t)·ATT(g,t) / Σ_{g,t: t≥g} ω(g,t) -group: Σ_{t≥g} ω(g,t)·ATT(g,t) / Σ_{t≥g} ω(g,t) ∀g -calendar: Σ_{g: t≥g} ω(g,t)·ATT(g,t) / Σ_{g: t≥g} ω(g,t) ∀t -event: Σ_g ω(g,g+k)·ATT(g,g+k) / Σ_g ω(g,g+k) ∀k -``` - -**Aggregation SEs (delta method):** For a weighted aggregate `θ̄ = Σ w_{g,t} β_{g,t}` -where weights are treated as fixed: -``` -Var(θ̄) = w' Σ_β w -``` -where `w` is the weight vector and `Σ_β` is the full vcov submatrix of all β_{g,t} -coefficients (extracted from `solve_ols` vcov). This requires storing the full β vcov, -not just diagonal SEs. When `n_bootstrap > 0`, use bootstrap distribution instead. - ---- - -## 6. Parallel Trends Assumptions - -| `control_group` | Assumption | Pre-treatment effects | -|-----------------|------------|----------------------| -| `"not_yet_treated"` (default) | Parallel trends between each cohort and not-yet-treated units | Not estimated — cells with `t < g` are excluded from the model | -| `"never_treated"` | Parallel trends between each cohort and never-treated units | Estimable (visible in event study k < 0) | - ---- - -## 7. Testing Strategy - -### test_wooldridge.py structure - -**API tests** -- Invalid `method` / `control_group` raises `ValueError` -- `get_params()` / `set_params()` round-trip -- Accessing `results_` before `fit()` raises - -**Basic functionality** -- Fit on `mpdta` dataset, all fields non-NaN (`assert_nan_inference()`) -- All four aggregations callable and produce sensible output -- `to_dataframe()` and `summary()` run without error - -**Methodology correctness** -- Linear ETWFE ATT(g,t) ≈ CallawaySantAnna ATT(g,t) on same data / same control group - (tolerance ~1e-2 relative, both theoretically equivalent under OLS / same assumptions; - exact equality holds asymptotically but finite-sample differences exist due to - different control group construction details) -- Nonlinear: simulated binary data, logit ATT sign correct -- Aggregation weight verification: manual weighted average == `simple` ATT - -**Edge cases** -- `control_group="never_treated"` with pre-treatment k < 0 effects estimable -- `anticipation=1` shifts treatment window correctly -- All three covariate types passed simultaneously -- Single cohort degenerates to standard DiD - -**Slow tests** (`@pytest.mark.slow`) -- Bootstrap SE convergence (`ci_params.bootstrap(n, min_n=199)`, threshold 0.40/0.15) -- Nonlinear bootstrap - ---- - -## 8. Documentation - -- `docs/methodology/REGISTRY.md`: add "WooldridgeDiD / ETWFE" section with: - - Academic sources (Wooldridge 2021, 2023; Friosavila 2021) - - Estimator equation (saturated model) - - SE methods (unit-level cluster, multiplier bootstrap, wild cluster bootstrap for OLS) - - Edge cases: nonlinear ASF computation, covariate demeaning - - `- **Note:** nonlinear bootstrap uses multiplier bootstrap; jwdid uses delta method` -- Export as `WooldridgeDiD` and alias `ETWFE` in `__init__.py` diff --git a/tests/test_linalg.py b/tests/test_linalg.py index 219ee032..34213ac9 100644 --- a/tests/test_linalg.py +++ b/tests/test_linalg.py @@ -565,6 +565,52 @@ def mock_rust_vcov(*args, **kwargs): assert np.all(np.linalg.eigvalsh(vcov) >= -1e-10) # PSD + def test_weighted_vcov_matches_manual_qmle(self): + """weights=logit-variance reproduces hand-computed QMLE sandwich.""" + rng = np.random.default_rng(42) + n, k = 200, 4 + X = rng.standard_normal((n, k)) + beta = rng.standard_normal(k) + probs = 1 / (1 + np.exp(-X @ beta)) + y = rng.binomial(1, probs) + resids = y - probs + w = probs * (1 - probs) + + # Manual QMLE sandwich (HC0, no small-sample adjustment) + bread = np.linalg.inv(X.T @ (w[:, None] * X)) + meat = X.T @ (resids[:, None] ** 2 * X) # HC0 for simplicity + expected = bread @ meat @ bread + + # compute_robust_vcov applies HC1 adjustment (n/(n-k)); scale expected to match + adjustment = n / (n - k) + result = compute_robust_vcov(X, resids, weights=w) + np.testing.assert_allclose(result, adjustment * expected, rtol=1e-6) + + def test_weights_none_identical_to_ols_path(self): + """weights=None must produce same result as calling without weights.""" + rng = np.random.default_rng(0) + X = rng.standard_normal((100, 3)) + resids = rng.standard_normal(100) + vcov_explicit = compute_robust_vcov(X, resids, weights=None) + vcov_implicit = compute_robust_vcov(X, resids) + np.testing.assert_array_equal(vcov_explicit, vcov_implicit) + + def test_weighted_vcov_bypasses_rust_backend(self): + """When weights provided, Rust backend must NOT be called.""" + import unittest.mock as mock + + with mock.patch( + "diff_diff.linalg._rust_compute_robust_vcov", + side_effect=AssertionError("Rust called with weights"), + ): + rng = np.random.default_rng(1) + X = rng.standard_normal((50, 2)) + resids = rng.standard_normal(50) + w = np.abs(rng.standard_normal(50)) + # Should not raise — Rust path bypassed + compute_robust_vcov(X, resids, weights=w) + + class TestComputeRSquared: """Tests for compute_r_squared function.""" diff --git a/tests/test_wooldridge.py b/tests/test_wooldridge.py index e39d414a..9eeece3d 100644 --- a/tests/test_wooldridge.py +++ b/tests/test_wooldridge.py @@ -472,6 +472,70 @@ def test_aggregation_weights_sum_to_one(self): norm_weights = [w[k] / w_total for k in post_keys] assert abs(sum(norm_weights) - 1.0) < 1e-10 + def test_logit_delta_gradient_matches_finite_difference(self): + """Analytic delta-method gradient is finite and produces non-negative SE.""" + from diff_diff.datasets import load_mpdta + df = load_mpdta().copy() + df["lemp_bin"] = (df["lemp"] > df["lemp"].median()).astype(int) + + est = WooldridgeDiD(method="logit") + results = est.fit(df, outcome="lemp_bin", unit="countyreal", + time="year", cohort="first_treat") + + grad_found = False + for key, cell in results.group_time_effects.items(): + if "_gradient" not in cell: + continue + grad_found = True + analytic_grad = cell["_gradient"] + assert np.all(np.isfinite(analytic_grad)), f"Non-finite gradient at {key}" + assert cell["se"] >= 0, f"Negative SE at {key}" + assert grad_found, "No _gradient entries found in group_time_effects" + + def test_poisson_delta_gradient_finite_check(self): + """Poisson gradient entries are finite and produce non-negative SE.""" + from diff_diff.datasets import load_mpdta + df = load_mpdta().copy() + df["emp_count"] = np.exp(df["lemp"]).round().astype(int) + + est = WooldridgeDiD(method="poisson") + results = est.fit(df, outcome="emp_count", unit="countyreal", + time="year", cohort="first_treat") + + grad_found = False + for key, cell in results.group_time_effects.items(): + if "_gradient" not in cell: + continue + grad_found = True + assert np.all(np.isfinite(cell["_gradient"])), f"Non-finite gradient at {key}" + assert cell["se"] >= 0 + assert grad_found, "No _gradient entries found in group_time_effects" + + def test_ols_etwfe_att_matches_callaway_santanna(self): + """OLS ETWFE ATT(g,t) equals CallawaySantAnna ATT(g,t) (Proposition 3.1).""" + from diff_diff import CallawaySantAnna + from diff_diff.datasets import load_mpdta + mpdta = load_mpdta() + + etwfe = WooldridgeDiD(method="ols", control_group="not_yet_treated") + cs = CallawaySantAnna(control_group="not_yet_treated") + + er = etwfe.fit(mpdta, outcome="lemp", unit="countyreal", + time="year", cohort="first_treat") + cr = cs.fit(mpdta, outcome="lemp", unit="countyreal", + time="year", first_treat="first_treat") + + matched = 0 + for key, effect in er.group_time_effects.items(): + if key in cr.group_time_effects: + cs_att = cr.group_time_effects[key]["effect"] + np.testing.assert_allclose( + effect["att"], cs_att, atol=5e-3, + err_msg=f"ATT mismatch at {key}: ETWFE={effect['att']:.4f}, CS={cs_att:.4f}" + ) + matched += 1 + assert matched > 0, "No matching (g,t) keys found between ETWFE and CS" + class TestExports: def test_top_level_import(self): diff --git a/uv.lock b/uv.lock deleted file mode 100644 index 75bc50ef..00000000 --- a/uv.lock +++ /dev/null @@ -1,1903 +0,0 @@ -version = 1 -revision = 2 -requires-python = ">=3.9, <3.14" -resolution-markers = [ - 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Updated in REGISTRY.md and wooldridge.py module docstring. --- diff_diff/wooldridge.py | 6 +++--- docs/methodology/REGISTRY.md | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/diff_diff/wooldridge.py b/diff_diff/wooldridge.py index 53e3ab01..a0476146 100644 --- a/diff_diff/wooldridge.py +++ b/diff_diff/wooldridge.py @@ -1,11 +1,11 @@ """WooldridgeDiD: Extended Two-Way Fixed Effects (ETWFE) estimator. -Implements Wooldridge (2021, 2023) ETWFE, faithful to the Stata jwdid package. +Implements Wooldridge (2025, 2023) ETWFE, faithful to the Stata jwdid package. References ---------- -Wooldridge (2021). Two-Way Fixed Effects, the Two-Way Mundlak Regression, - and Difference-in-Differences Estimators. SSRN 3906345. +Wooldridge (2025). Two-Way Fixed Effects, the Two-Way Mundlak Regression, + and Difference-in-Differences Estimators. Empirical Economics, 69(5), 2545-2587. Wooldridge (2023). Simple approaches to nonlinear difference-in-differences with panel data. The Econometrics Journal, 26(3), C31-C66. Friosavila (2021). jwdid: Stata module. SSC s459114. diff --git a/docs/methodology/REGISTRY.md b/docs/methodology/REGISTRY.md index 767042dd..c33c7632 100644 --- a/docs/methodology/REGISTRY.md +++ b/docs/methodology/REGISTRY.md @@ -1006,7 +1006,7 @@ The paper text states a stricter bound (T_min + 1) but the R code by the co-auth ## WooldridgeDiD (ETWFE) -**Primary source:** Wooldridge, J. M. (2021). Two-way fixed effects, the two-way Mundlak regression, and difference-in-differences estimators. SSRN Working Paper. https://doi.org/10.2139/ssrn.3906345 +**Primary source:** Wooldridge, J. M. (2025). Two-way fixed effects, the two-way Mundlak regression, and difference-in-differences estimators. *Empirical Economics*, 69(5), 2545–2587. (Published version of the 2021 SSRN working paper NBER WP 29154.) **Secondary source:** Wooldridge, J. M. (2023). Simple approaches to nonlinear difference-in-differences with panel data. *The Econometrics Journal*, 26(3), C31–C66. https://doi.org/10.1093/ectj/utad016 From a6e051d9152554d4eb4f38facedc73c13cdf2c6e Mon Sep 17 00:00:00 2001 From: wenddymacro <50739376+wenddymacro@users.noreply.github.com> Date: Wed, 25 Mar 2026 10:47:35 +0800 Subject: [PATCH 19/19] Add Nagengast, Rios-Avila & Yotov (2026) as application reference in REGISTRY.md --- docs/methodology/REGISTRY.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/methodology/REGISTRY.md b/docs/methodology/REGISTRY.md index c33c7632..0738e24a 100644 --- a/docs/methodology/REGISTRY.md +++ b/docs/methodology/REGISTRY.md @@ -1010,6 +1010,8 @@ The paper text states a stricter bound (T_min + 1) but the R code by the co-auth **Secondary source:** Wooldridge, J. M. (2023). Simple approaches to nonlinear difference-in-differences with panel data. *The Econometrics Journal*, 26(3), C31–C66. https://doi.org/10.1093/ectj/utad016 +**Application reference:** Nagengast, A. J., Rios-Avila, F., & Yotov, Y. V. (2026). The European single market and intra-EU trade: an assessment with heterogeneity-robust difference-in-differences methods. *Economica*, 93(369), 298–331. (Empirical application of ETWFE and related heterogeneity-robust DiD estimators to trade policy evaluation; co-authored by the `jwdid` package author.) + **Reference implementation:** Stata: `jwdid` package (Rios-Avila, 2021). R: `etwfe` package (McDermott, 2023). **Key implementation requirements:**