linearmodels.panel.covariance.HomoskedasticCovariance¶

class linearmodels.panel.covariance.HomoskedasticCovariance(y: ndarray[tuple[int, ...], dtype[float64]], x: ndarray[tuple[int, ...], dtype[float64]], params: ndarray[tuple[int, ...], dtype[float64]], entity_ids: ndarray[tuple[int, ...], dtype[int64]] | None, time_ids: ndarray[tuple[int, ...], dtype[int64]] | None, *, debiased: bool = False, extra_df: int = 0)[source]¶

Homoskedastic covariance estimation

Parameters:¶

y: ndarray[tuple[int, ...], dtype[float64]]¶: (entity x time) by 1 stacked array of dependent
x: ndarray[tuple[int, ...], dtype[float64]]¶: (entity x time) by variables stacked array of exogenous
params: ndarray[tuple[int, ...], dtype[float64]]¶: variables by 1 array of estimated model parameters
entity_ids: ndarray[tuple[int, ...], dtype[int64]] | None¶: (entity x time) by 1 stacked array of entity ids
time_ids: ndarray[tuple[int, ...], dtype[int64]] | None¶: (entity x time) by 1 stacked array of time ids
debiased: bool = False¶: Flag indicating whether to debias the estimator
extra_df: int = 0¶: Additional degrees of freedom consumed by models beyond the number of columns in x, e.g., fixed effects. Covariance estimators are always adjusted for extra_df irrespective of the setting of debiased

Notes

The estimator of the covariance is

\[s^2\hat{\Sigma}_{xx}^{-1}\]

where

\[\hat{\Sigma}_{xx} = X'X\]

and

\[s^2 = (n-df)^{-1} \hat{\epsilon}'\hat{\epsilon}\]

where df is extra_df and n-df is replace by n-df-k if debiased is True.

Methods

`deferred_cov`()	Covariance calculation deferred until executed

Properties