linearmodels.panel.model.FamaMacBeth.fit¶

FamaMacBeth.fit(cov_type: str = 'unadjusted', debiased: bool = True, bandwidth: float | None = None, kernel: str | None = None) → FamaMacBethResults[source]¶

Estimate model parameters

Parameters:¶

cov_type: str = 'unadjusted'¶

Name of covariance estimator (see notes). Default is “unadjusted”.

debiased: bool = True¶

Flag indicating whether to debiased the covariance estimator using a degree of freedom adjustment.

bandwidth: float | None = None¶

The bandwidth to use when cov_type is “kernel”. If None, it is automatically computed.

kernel: str | None = None¶

The kernel to use. None chooses the default kernel.

Returns:¶

Estimation results

Return type:¶

linearmodels.panel.results.PanelResults

Examples

>>> from linearmodels import FamaMacBeth
>>> mod = FamaMacBeth(y, x)
>>> res = mod.fit(cov_type="kernel", kernel="Parzen")

Notes

Two covariance estimators are supported:

• “unadjusted”, “homoskedastic”, “robust”, “heteroskedastic” use the standard covariance estimator of the T parameter estimates.

• “kernel” is a HAC estimator. Configurations options are: