linearmodels.panel.model.BetweenOLS.fit¶

BetweenOLS.fit(*, reweight: bool = False, cov_type: str = 'unadjusted', debiased: bool = True, **cov_config: bool | float | str | ndarray | DataFrame | PanelData) → PanelResults[source]¶

Estimate model parameters

Parameters:¶

reweight: bool = False¶

Flag indicating to reweight observations if the input data is unbalanced using a WLS estimator. If weights are provided, these are accounted for when reweighting. Has no effect on balanced data.

cov_type: str = 'unadjusted'¶

Name of covariance estimator. See Notes.

debiased: bool = True¶

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

**cov_config

Additional covariance-specific options. See Notes.

Returns:¶

Estimation results

Return type:¶

linearmodels.panel.results.PanelResults

Examples

>>> from linearmodels import BetweenOLS
>>> mod = BetweenOLS(y, x)
>>> res = mod.fit(cov_type='robust')

Notes

Three covariance estimators are supported:

• “unadjusted”, “homoskedastic” - Assume residual are homoskedastic

• “robust”, “heteroskedastic” - Control for heteroskedasticity using White’s estimator

• “clustered` - One- or two-way clustering. Configuration options are:

  • clusters - Input containing 1 or 2 variables. Clusters should be integer values, although other types will be coerced to integer values by treating as categorical variables

When using a clustered covariance estimator, all cluster ids must be identical within an entity.