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BUG: Raise for ignored flags in out-of-sample panel predictions #849

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Merged
bashtage merged 1 commit into from
Oct 18, 2026
Merged

BUG: Raise for ignored flags in out-of-sample panel predictions #849

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12 changes: 10 additions & 2 deletions linearmodels/panel/results.py
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Original file line number Diff line number Diff line change
Expand Up @@ -605,8 +605,9 @@ def predict(
Raises
------
ValueError
If both `exog` and `data` are provided, or if no in-sample output
is selected.
If both `exog` and `data` are provided, if no in-sample output
is selected, or if `effects` or `idiosyncratic` is requested for
out-of-sample predictions.

Notes
-----
Expand All @@ -621,6 +622,13 @@ def predict(
predictions.
"""
if not (exog is None and data is None):
if effects or idiosyncratic:
raise ValueError(
"effects and idiosyncratic are not available for "
"out-of-sample predictions"
)
if not fitted:
raise ValueError("At least one output must be selected")
context = capture_context(1)
return self._out_of_sample(exog, data, missing, context=context)
out = []
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21 changes: 21 additions & 0 deletions linearmodels/tests/panel/test_results.py
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Original file line number Diff line number Diff line change
Expand Up @@ -387,6 +387,27 @@ def test_predict_no_selection(generated_data):
res.predict(fitted=False, effects=False, idiosyncratic=False, missing=True)


def test_predict_out_of_sample_flags(generated_data):
if np.any(np.isnan(generated_data.x)):
pytest.skip("Cannot test with missing values")
with warns_if_missing(generated_data.y, generated_data.x):
mod = PanelOLS(generated_data.y, generated_data.x, entity_effects=True)
res = mod.fit()
exog = generated_data.x
with pytest.raises(
ValueError, match=r"not available for out-of-sample predictions"
):
res.predict(exog, effects=True)
with pytest.raises(
ValueError, match=r"not available for out-of-sample predictions"
):
res.predict(exog, idiosyncratic=True)
with pytest.raises(ValueError, match=r"At least one output must be"):
res.predict(exog, fitted=False)
pred = res.predict(exog)
assert list(pred.columns) == ["predictions"]


@pytest.mark.parametrize(
"constraint_formula",
[
Expand Down