linearmodels.system.results.GMMSystemResults¶

class linearmodels.system.results.GMMSystemResults(results: AttrDict)[source]¶

Results from GMM System Estimators

Parameters:¶

Methods

`breusch_pagan`()	Breusch-Pagan LM test for no cross-correlation
`conf_int`([level])	Confidence interval construction
`likelihood_ratio`()	Likelihood ratio test of no cross-correlation
`predict`([equations, data, fitted, ...])	In- and out-of-sample predictions

Properties

`cov`	Estimated covariance of parameters
`cov_config`	Configuration of covariance estimator used to compute covariance
`cov_estimator`	Type of covariance estimator used to compute covariance
`debiased`	Flag indicating whether covariance uses a small-sample adjustment
`df_model`	Model degree of freedom
`df_resid`	Residual degree of freedom
`equation_labels`	Individual equation labels
`equations`	Individual equation results
`fitted_values`	Fitted values
`iterations`	Number of iterations of the GLS executed
`j_stat`	J-test of overidentifying restrictions
`method`	Estimation method
`model`	Model used in estimation
`model_ss`	Residual sum of squares
`nobs`	Number of observations
`params`	Estimated parameters
`pvalues`	Parameter p-vals.
`resid_ss`	Residual sum of squares
`resids`	Estimated residuals
`rsquared`	Coefficient of determination (R2)
`sigma`	Estimated residual covariance
`std_errors`	Estimated parameter standard errors
`summary`	Model estimation summary.
`system_rsquared`	Alternative measure of system fit
`total_ss`	Total sum of squares
`tstats`	Parameter t-statistics
`w`	GMM weight matrix used in estimation
`weight_config`	Weight configuration options used in GMM estimation
`weight_type`	Type of weighting used in GMM estimation
`wresids`	Weighted estimated residuals