learn_hausman_test

source

learn_hausman_test(
    n_units=60,
    n_periods=8,
    beta=1.0,
    unit_effect_corr=0.8,
    seed=0,
)

Show why the Hausman test prefers fixed effects when unit effects are correlated.

Simulates a panel where the regressor x is correlated with each unit’s effect. Random effects assumes no such correlation (so it is biased here) while fixed effects is consistent; the Hausman test compares the two and rejects their equality.

Parameters

Name	Type	Description	Default
n_units	int	Panel dimensions.	`60`
n_periods	int	Panel dimensions.	`60`
beta	float	True within-unit slope.	`1.0`
unit_effect_corr	float	Correlation between `x` and the unit effect (drives the random-effects bias).	`0.8`
seed	int	Random seed.	`0`

Returns

Name	Type	Description
	SandboxResult	`df` (FE vs RE vs true slope), `fig`, `summary` and `topic`.

Examples

This sandbox simulates its own panel, so the call needs no DataFrame:

import expdpy as ex

res = ex.learn_hausman_test()
res.fig