analyze_hausman_test

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analyze_hausman_test(df, dv, idvs, *, entity=None, time=None)

Run the Hausman test comparing fixed-effects and random-effects estimates.

Parameters

Name	Type	Description	Default
df	pd.DataFrame	Long panel data frame.	required
dv	str	Dependent variable name.	required
idvs	Sequence[str] \| str	Independent variable name(s).	required
entity	str \| None	The cross-section and time identifiers. Default to the panel declared via :func:`expdpy.set_panel` / :func:`expdpy.set_labels` (`set_panel=True`).	`None`
time	str \| None	The cross-section and time identifiers. Default to the panel declared via :func:`expdpy.set_panel` / :func:`expdpy.set_labels` (`set_panel=True`).	`None`

Returns

Name	Type	Description
	HausmanTestResult	The test statistic, degrees of freedom, p-value and the compared coefficients.

Examples

Basic — compare the FE and RE estimates. With the panel declared by set_panel=True, entity and time are resolved automatically.

import expdpy as ex
from expdpy.data import load_kuznets, load_kuznets_data_def

df = ex.set_labels(load_kuznets(), load_kuznets_data_def(), set_panel=True)
ht = ex.analyze_hausman_test(df, dv="gini_regional", idvs=["log_gdp_pc"])
ht.statistic, ht.df_test, ht.p_value

(13.65930971955897, 1, 0.00021915204898468124)

Advanced — several regressors, then read the compared coefficients side by side.

import expdpy as ex
from expdpy.data import load_kuznets, load_kuznets_data_def

df = ex.set_labels(load_kuznets(), load_kuznets_data_def(), set_panel=True)
ht = ex.analyze_hausman_test(
    df, dv="gini_regional", idvs=["log_gdp_pc", "trade_share"]
)
ht.fe_coefs         # fixed-effects estimates
ht.re_coefs         # random-effects estimates
print(ht.interpret())

Hausman test (χ²(2) = 14.400, p = 0.0007468): **reject** the null — the random-effects assumption is violated, so prefer **fixed effects**. Note that failing to reject reflects a lack of evidence against random effects, not proof that it is correct.