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Merged
bashtage merged 1 commit into from
Jul 21, 2025
Merged

MAINT: Fixes for pandas and flake #647

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3 changes: 2 additions & 1 deletion linearmodels/system/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -102,7 +102,7 @@ def _missing_weights(
missing = [key for key in weights if weights[key] is None]
if missing:
msg = "Weights not found for equation labels:\n{}".format(", ".join(missing))
warnings.warn(msg, UserWarning)
warnings.warn(msg, UserWarning, stacklevel=2)


def _parameters_from_xprod(
Expand Down Expand Up @@ -1917,6 +1917,7 @@ def __init__(
"matrix not unadjusted (homoskedastic). sigma will "
"be ignored.",
UserWarning,
stacklevel=1,
)
weight_type = COV_TYPES[weight_type]
self._weight_est = GMM_W_EST[weight_type](**weight_config)
Expand Down
20 changes: 15 additions & 5 deletions linearmodels/tests/panel/_utility.py
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Original file line number Diff line number Diff line change
Expand Up @@ -200,13 +200,23 @@ def generate_data(


def assert_results_equal(res1, res2, test_fit=True, test_df=True, strict=True):
from pandas import Index

def fix_index(x: Series | DataFrame, n: int):
if isinstance(x, Series):
out = x.iloc[:n].copy()
else:
out = x.iloc[:n, :n].copy()
out.index = Index(out.index.to_list())
return out

n = min(res1.params.shape[0], res2.params.shape[0])

assert_series_equal(res1.params.iloc[:n], res2.params.iloc[:n])
assert_series_equal(res1.pvalues.iloc[:n], res2.pvalues.iloc[:n])
assert_series_equal(res1.tstats.iloc[:n], res2.tstats.iloc[:n])
assert_frame_equal(res1.cov.iloc[:n, :n], res2.cov.iloc[:n, :n])
assert_frame_equal(res1.conf_int().iloc[:n], res2.conf_int().iloc[:n])
assert_series_equal(res1.params.iloc[:n], fix_index(res2.params, n))
assert_series_equal(res1.pvalues.iloc[:n], fix_index(res2.pvalues, n))
assert_series_equal(res1.tstats.iloc[:n], fix_index(res2.tstats, n))
assert_frame_equal(res1.cov.iloc[:n, :n], fix_index(res2.cov, n))
assert_frame_equal(res1.conf_int().iloc[:n], fix_index(res2.conf_int(), n))

assert_allclose(res1.s2, res2.s2)

Expand Down
28 changes: 22 additions & 6 deletions linearmodels/tests/panel/test_panel_ols.py
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Original file line number Diff line number Diff line change
Expand Up @@ -286,7 +286,9 @@ def test_panel_entity_lsdv(data):
d_demean = d.values

xd = np.c_[x.values, d_demean]
xd = pd.DataFrame(xd, index=x.index, columns=list(x.columns) + list(d.columns))
xd = pd.DataFrame(
xd, index=x.index, columns=list(x.columns) + [f"fe_{col}" for col in d.columns]
)

ols_mod = IV2SLS(y, xd, None, None)
res2 = ols_mod.fit(cov_type="unadjusted", debiased=False)
Expand Down Expand Up @@ -388,7 +390,9 @@ def test_panel_time_lsdv(large_data):
d = d - z @ lstsq(z, d, rcond=None)[0]

xd = np.c_[x.values, d]
xd = pd.DataFrame(xd, index=x.index, columns=list(x.columns) + d_cols)
xd = pd.DataFrame(
xd, index=x.index, columns=list(x.columns) + [f"fe_{col}" for col in d_cols]
)

ols_mod = IV2SLS(y, xd, None, None)
res2 = ols_mod.fit(cov_type="unadjusted")
Expand Down Expand Up @@ -490,7 +494,11 @@ def test_panel_both_lsdv(data):

xd = np.c_[x.values, d]
xd = pd.DataFrame(
xd, index=x.index, columns=list(x.columns) + list(d1.columns) + list(d2.columns)
xd,
index=x.index,
columns=list(x.columns)
+ [f"fe_{col}" for col in d1.columns]
+ [f"te_{col}" for col in d2.columns],
)

ols_mod = IV2SLS(y, xd, None, None)
Expand Down Expand Up @@ -597,7 +605,9 @@ def test_panel_entity_lsdv_weighted(data):
d = d - z @ lstsq(wz, wd, rcond=None)[0]

xd = np.c_[x.values, d]
xd = pd.DataFrame(xd, index=x.index, columns=list(x.columns) + list(d_cols))
xd = pd.DataFrame(
xd, index=x.index, columns=list(x.columns) + [f"fe_{col}" for col in d_cols]
)

ols_mod = IV2SLS(y, xd, None, None, weights=w)
res2 = ols_mod.fit(cov_type="unadjusted")
Expand Down Expand Up @@ -677,7 +687,9 @@ def test_panel_time_lsdv_weighted(large_data):
d = d - z @ lstsq(wz, wd, rcond=None)[0]

xd = np.c_[x.values, d]
xd = pd.DataFrame(xd, index=x.index, columns=list(x.columns) + list(d_cols))
xd = pd.DataFrame(
xd, index=x.index, columns=list(x.columns) + [f"te_{col}" for col in d_cols]
)

ols_mod = IV2SLS(y, xd, None, None, weights=w)
res2 = ols_mod.fit(cov_type="unadjusted")
Expand Down Expand Up @@ -760,7 +772,11 @@ def test_panel_both_lsdv_weighted(data):

xd = np.c_[x.values, d]
xd = pd.DataFrame(
xd, index=x.index, columns=list(x.columns) + list(d1.columns) + list(d2.columns)
xd,
index=x.index,
columns=list(x.columns)
+ [f"fe_{col}" for col in d1.columns]
+ [f"te_{col}" for col in d2.columns],
)

ols_mod = IV2SLS(y, xd, None, None, weights=w)
Expand Down
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