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Fix fweights bug #851

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Fix fweights bug #851

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2 changes: 2 additions & 0 deletions pyfixest/estimation/feols_.py
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Original file line number Diff line number Diff line change
Expand Up @@ -734,6 +734,8 @@ def _vcov_iid(self):

def _vcov_hetero(self):
_scores = self._scores
if self._weights_type == "fweights":
_scores /= np.sqrt(self._weights) # remove one sqrt(w)
_vcov_type_detail = self._vcov_type_detail
_tXZ = self._tXZ
_tZZinv = self._tZZinv
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65 changes: 24 additions & 41 deletions tests/test_wls_types.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,55 +4,38 @@
import pyfixest as pf


# @pytest.mark.skip(reason="Bug for fweights and heteroskedastic errors.")
def test_fweights_ols():
"Test that the fweights are correctly implemented for OLS models."
# Fepois model for discrete Y
data = pf.get_data(model="Fepois")
data2_w = (
data[["Y", "X1"]]
.groupby(["Y", "X1"])
.size()
.reset_index()
.rename(columns={0: "count"})
@pytest.mark.parametrize("fml", ["Y ~ X1", "Y ~ X1 | f1"])
@pytest.mark.parametrize("vcov", ["iid", "hetero", {"CRV1": "f1"}])
def test_fweights_ols_equivalence(fml, vcov):
"Test that frequency weights yield same result as long data for various vcov types."
data = pf.get_data(model="Fepois").dropna()

# Compressed versions
group_vars = (
["Y", "X1", "f1"] if "f1" in fml or vcov == {"CRV1": "f1"} else ["Y", "X1"]
)
data3_w = (
data[["Y", "X1", "f1"]]
.groupby(["Y", "X1", "f1"])

data_w = (
data[group_vars]
.groupby(group_vars)
.size()
.reset_index()
.rename(columns={0: "count"})
)

fit1 = pf.feols("Y ~ X1", data=data, ssc=pf.ssc(adj=False, cluster_adj=False))
fit2 = pf.feols(
"Y ~ X1",
data=data2_w,
weights="count",
weights_type="fweights",
ssc=pf.ssc(adj=False, cluster_adj=False),
# Fit models
fit_long = pf.feols(fml, data=data, vcov=vcov)
fit_compressed = pf.feols(
fml, data=data_w, vcov=vcov, weights="count", weights_type="fweights"
)

# Assert point estimates match
np.testing.assert_allclose(
fit_long.tidy().values, fit_compressed.tidy().values, rtol=1e-6, atol=1e-8
)

assert fit1._N == fit2._N, "Number of observations is not the same."

if False:
np.testing.assert_allclose(fit1.tidy().values, fit2.tidy().values)

np.testing.assert_allclose(fit1.vcov("HC1")._vcov, fit2.vcov("HC1")._vcov)
np.testing.assert_allclose(fit1.vcov("HC2")._vcov, fit2.vcov("HC2")._vcov)
np.testing.assert_allclose(fit1.vcov("HC3")._vcov, fit2.vcov("HC3")._vcov)

fit3 = pf.feols("Y ~ X1 | f1", data=data)
fit4 = pf.feols(
"Y ~ X1 | f1", data=data3_w, weights="count", weights_type="fweights"
)
np.testing.assert_allclose(fit3.tidy().values, fit4.tidy().values)
np.testing.assert_allclose(
fit3.vcov({"CRV3": "f1"})._vcov, fit4.vcov({"CRV3": "f1"})._vcov
)
np.testing.assert_allclose(fit1.vcov("HC1")._vcov, fit2.vcov("HC1")._vcov)
np.testing.assert_allclose(fit1.vcov("HC2")._vcov, fit2.vcov("HC2")._vcov)
np.testing.assert_allclose(fit1.vcov("HC3")._vcov, fit2.vcov("HC3")._vcov)
# Optional: also check N is the same
assert fit_long._N == fit_compressed._N, "Sample size mismatch"


@pytest.mark.skip(reason="Not implemented yet.")
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