learn_correlated_random_effects

source

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

Show the Mundlak (correlated random effects) device recovering the fixed-effects slope.

The CRE model adds each unit’s time-mean of the regressor to a random-effects specification. Its coefficient on the original x then equals the within (fixed-effects) estimate, and a Wald test on the added means is the Mundlak (Hausman-equivalent) test.

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.	`0.8`
seed	int	Random seed.	`0`

Returns

Name	Type	Description
	SandboxResult	`df` (CRE-within vs plain FE 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_correlated_random_effects()
res.fig