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

Estimate model 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_cov : bool

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

iterate : bool

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 : bool

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


results – Estimation results

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