linearmodels.system.model.IV3SLS.fit

IV3SLS.fit(*, method: Literal['ols', 'gls', None] | None = None, full_cov: bool = True, iterate: bool = False, iter_limit: int = 100, tol: float = 1e-06, cov_type: str = 'robust', **cov_config: bool) SystemResults

Estimate model parameters

Parameters:
method: Literal['ols', 'gls', None] | None = None

Estimation method. Default auto selects based on regressors, using OLS only if all regressors are identical. The other two arguments force the use of GLS or OLS.

full_cov: bool = True

Flag indicating whether to utilize information in correlations when estimating the model with GLS

iterate: bool = False

Flag indicating to iterate GLS until convergence of iter limit iterations have been completed

iter_limit: int = 100

Maximum number of iterations for iterative GLS

tol: float = 1e-06

Tolerance to use when checking for convergence in iterative GLS

cov_type: str = 'robust'

Name of covariance estimator. Valid options are

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

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

  • ”kernel” - Allows for heteroskedasticity and autocorrelation

  • ”clustered” - Allows for 1 and 2-way clustering of errors (Rogers).

**cov_config

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

Returns:

results – Estimation results

Return type:

linearmodels.system.results.SystemResults