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Use vendored data in Hansen-Singleton lectures #972

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9 changes: 4 additions & 5 deletions lectures/hansen_singleton_1982.md
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Original file line number Diff line number Diff line change
Expand Up @@ -985,19 +985,18 @@ Because the Ken French return is not identical to the original CRSP NYSE value-w

Both this lecture and the companion lecture {doc}`hansen_singleton_1983` use the same data construction.

The hidden cell below loads a vendored monthly dataset of gross real returns and gross consumption growth. The data are built from the [FRED](https://fred.stlouisfed.org/) and [Ken French](https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html) data libraries by the maintenance script at [`_static/lecture_specific/hansen_singleton_1982/make_data.py`](https://github.com/QuantEcon/lecture-python.myst/blob/main/lectures/_static/lecture_specific/hansen_singleton_1982/make_data.py) and read here directly from GitHub.
The hidden cell below loads a vendored monthly dataset of gross real returns and gross consumption growth. The data are built from the [FRED](https://fred.stlouisfed.org/) and [Ken French](https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html) data libraries by the maintenance script at [`_static/lecture_specific/hansen_singleton_1982/make_data.py`](https://github.com/QuantEcon/lecture-python.myst/blob/main/lectures/_static/lecture_specific/hansen_singleton_1982/make_data.py) and read here from the checked-out copy.

```{code-cell} ipython3
:tags: [hide-cell]

DATA_URL = (
"https://github.com/QuantEcon/lecture-python.myst/raw/refs/heads/main/"
"lectures/_static/lecture_specific/hansen_singleton_1982/"
DATA_PATH = (
"_static/lecture_specific/hansen_singleton_1982/"
"hansen_singleton_1982_data.csv"
)

# Read the vendored snapshot once; load_hs_monthly_data just slices it.
_data = pd.read_csv(DATA_URL, index_col=0, parse_dates=True)
_data = pd.read_csv(DATA_PATH, index_col=0, parse_dates=True)


def load_hs_monthly_data(start="1959-02-01", end="1978-12-01"):
Expand Down
11 changes: 5 additions & 6 deletions lectures/hansen_singleton_1983.md
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Original file line number Diff line number Diff line change
Expand Up @@ -1425,19 +1425,18 @@ While Hansen-Singleton use CRSP value-weighted NYSE returns, we use the Ken Fren

The consumption series is constructed from consumption of nondurables (`ND`) with the nondurables deflator.

The hidden cell below loads a vendored monthly dataset of returns and consumption series. The data are built from the [FRED](https://fred.stlouisfed.org/) and [Ken French](https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html) data libraries by the maintenance script at [`_static/lecture_specific/hansen_singleton_1983/make_data.py`](https://github.com/QuantEcon/lecture-python.myst/blob/main/lectures/_static/lecture_specific/hansen_singleton_1983/make_data.py) and read here directly from GitHub.
The hidden cell below loads a vendored monthly dataset of returns and consumption series. The data are built from the [FRED](https://fred.stlouisfed.org/) and [Ken French](https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html) data libraries by the maintenance script at [`_static/lecture_specific/hansen_singleton_1983/make_data.py`](https://github.com/QuantEcon/lecture-python.myst/blob/main/lectures/_static/lecture_specific/hansen_singleton_1983/make_data.py) and read here from the checked-out copy.

```{code-cell} ipython3
:tags: [hide-cell]

DATA_URL = (
"https://github.com/QuantEcon/lecture-python.myst/raw/refs/heads/main/"
"lectures/_static/lecture_specific/hansen_singleton_1983/"
DATA_PATH = (
"_static/lecture_specific/hansen_singleton_1983/"
"hansen_singleton_1983_data.csv"
)

# Read the vendored snapshot once; load_hs_monthly_data just slices it.
_data = pd.read_csv(DATA_URL, index_col=0, parse_dates=True)
_data = pd.read_csv(DATA_PATH, index_col=0, parse_dates=True)


def load_hs_monthly_data(start="1959-02-01", end="1978-12-01"):
Expand Down Expand Up @@ -1828,4 +1827,4 @@ Hansen and Singleton specified less about the joint distribution of returns an

They formulated an incomplete probability model that stops short of specifying a likelihood function.

To proceed, they used a generalized methods of moments (GMM) estimator to estimate key parameters that appear in the Euler equation.
To proceed, they used a generalized methods of moments (GMM) estimator to estimate key parameters that appear in the Euler equation.