linearmodels.panel.covariance.FamaMacBethCovariance¶

class linearmodels.panel.covariance.FamaMacBethCovariance(y: ndarray, x: ndarray, params: ndarray, all_params: DataFrame, *, debiased: bool = False, bandwidth: float | None = None, kernel: str | None = None)[source]¶

HAC estimator for Fama-MacBeth estimator

Parameters:¶

y: ndarray¶: (entity x time) by 1 stacked array of dependent
x: ndarray¶: (entity x time) by variables stacked array of exogenous
params: ndarray¶: (variables by 1) array of estimated model parameters
all_params: DataFrame¶: (nobs by variables) array of all estimated model parameters
debiased: bool = False¶: Flag indicating whether to debias the estimator.
bandwidth: float | None = None¶: Non-negative integer to use as bandwidth. Set to 0 to disable autocorrelation robustness. If not provided a rule-of- thumb value is used.
kernel: str | None = None¶: Name of one of the supported kernels. If None, uses the Newey-West kernel.

Notes

Covariance is a Kernel covariance of all estimated parameters.

Methods

`deferred_cov`()	Covariance calculation deferred until executed

Properties

`ALLOWED_KWARGS`
`DEFAULT_KERNEL`
`all_params`	The set of parameters estimated for each of the time periods
`bandwidth`	Estimator bandwidth
`cov`	Estimated covariance
`eps`	Model residuals
`name`	Covariance estimator name
`s2`	Error variance