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_limit
int
Maximum number of iterations for iterative GLS
- tol
float
Tolerance to use when checking for convergence in iterative GLS
- cov_type
str
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
- method{
- Returns
- results
SystemResults
Estimation results
- results
See also
- Return type