linearmodels.system.model.SUR.fit¶

SUR.fit(*, method: '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) → linearmodels.system.results.SystemResults¶

Estimate model parameters

Parameters:¶

method: '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: bool¶

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