linearmodels.system.model.SUR.fit

SUR.fit(*, method=None, full_cov=True, iterate=False, iter_limit=100, tol=1e-06, cov_type='robust', **cov_config)

Estimate model parameters

Parameters:
method{None, “gls”, “ols”}

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_covbool

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

iteratebool

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

iter_limitint

Maximum number of iterations for iterative GLS

tolfloat

Tolerance to use when checking for convergence in iterative GLS

cov_typestr

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:
resultsSystemResults

Estimation results

Return type:

SystemResults