linearmodels.iv.model.IV2SLS.fit¶

IV2SLS.fit(*, cov_type: str = 'robust', debiased: bool = False, **cov_config: Any) → OLSResults | IVResults¶

Estimate model parameters

Parameters:¶

cov_type: str = 'robust'¶

Name of covariance estimator to use. Supported covariance estimators are:

• ”unadjusted”, “homoskedastic” - Classic homoskedastic inference

• ”robust”, “heteroskedastic” - Heteroskedasticity robust inference

• ”kernel” - Heteroskedasticity and autocorrelation robust inference

• ”cluster” - One-way cluster dependent inference. Heteroskedasticity robust

debiased: bool = False¶

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

**cov_config: Any¶

Additional parameters to pass to covariance estimator. The list of optional parameters differ according to cov_type. See the documentation of the alternative covariance estimators for the complete list of available commands.

Returns:¶

Results container

Return type:¶

linearmodels.iv.results.IVResults

Notes

Additional covariance parameters depend on specific covariance used. The see the docstring of specific covariance estimator for a list of supported options. Defaults are used if no covariance configuration is provided.

See also

linearmodels.iv.covariance.HomoskedasticCovariance, linearmodels.iv.covariance.HeteroskedasticCovariance, linearmodels.iv.covariance.KernelCovariance, linearmodels.iv.covariance.ClusteredCovariance