linearmodels.system.model.IVSystemGMM.fit¶

IVSystemGMM.fit(*, iter_limit: int = 2, tol: float = 1e-06, initial_weight: ndarray[tuple[int, ...], dtype[float64]] | None = None, cov_type: str = 'robust', **cov_config: bool | float) → linearmodels.system.results.GMMSystemResults[source]¶

Estimate model parameters

Parameters:¶

iter_limit: int = 2¶

Maximum number of iterations for iterative GLS

tol: float = 1e-06¶

Tolerance to use when checking for convergence in iterative GLS

initial_weight: ndarray[tuple[int, ...], dtype[float64]] | None = None¶

Initial weighting matrix to use in the first step. If not specified, uses the average outer-product of the set containing the exogenous variables and instruments.

cov_type: str = 'robust'¶

Name of covariance estimator. Valid options are

• ”unadjusted”, “homoskedastic” - Classic covariance estimator

• ”robust”, “heteroskedastic” - Heteroskedasticity robust covariance estimator

**cov_config: bool | float¶

Additional parameters to pass to covariance estimator. All estimators support debiased which employs a small-sample adjustment

Returns:¶

Estimation results

Return type:¶

linearmodels.system.results.GMMSystemResults