7.12.1.5. statsmodels.sandbox.regression.gmm.IVGMMResults¶
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class
statsmodels.sandbox.regression.gmm.IVGMMResults(*args, **kwds)[source]¶ -
__init__(*args, **kwds)¶
Methods
__init__(*args, **kwds)calc_cov_params(moms, gradmoms[, weights, ...])calculate covariance of parameter estimates compare_j(other)overidentification test for comparing two nested gmm estimates conf_int([alpha, cols, method])Returns the confidence interval of the fitted parameters. cov_params(**kwds)f_test(r_matrix[, cov_p, scale, invcov])Compute the F-test for a joint linear hypothesis. fittedvalues()get_bse(**kwds)standard error of the parameter estimates with options initialize(model, params, **kwd)jtest()overidentification test jval()llf()load(fname)load a pickle, (class method) normalized_cov_params()predict([exog, transform])Call self.model.predict with self.params as the first argument. pvalues()q()remove_data()remove data arrays, all nobs arrays from result and model resid()save(fname[, remove_data])save a pickle of this instance ssr()summary([yname, xname, title, alpha])Summarize the Regression Results t_test(r_matrix[, cov_p, scale, use_t])Compute a t-test for a each linear hypothesis of the form Rb = q tvalues()Return the t-statistic for a given parameter estimate. wald_test(r_matrix[, cov_p, scale, invcov, ...])Compute a Wald-test for a joint linear hypothesis. Attributes
bsestandard error of the parameter estimates use_t-