statsmodels.sandbox.regression.gmm.GMMResults¶
-
class
statsmodels.sandbox.regression.gmm.
GMMResults
(*args, **kwds)[source]¶ just a storage class right now
Attributes
bse_
standard error of the parameter estimates Methods
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])Construct confidence interval for the fitted parameters. cov_params
([r_matrix, column, scale, cov_p, …])Compute the variance/covariance matrix. f_test
(r_matrix[, cov_p, scale, invcov])Compute the F-test for a joint linear hypothesis. get_bse
(**kwds)standard error of the parameter estimates with options initialize
(model, params, **kwargs)Initialize (possibly re-initialize) a Results instance. jtest
()overidentification test load
(fname)Load a pickled results instance normalized_cov_params
()See specific model class docstring predict
([exog, transform])Call self.model.predict with self.params as the first argument. remove_data
()Remove data arrays, all nobs arrays from result and model. save
(fname[, remove_data])Save a pickle of this instance. 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. t_test_pairwise
(term_name[, method, alpha, …])Perform pairwise t_test with multiple testing corrected p-values. wald_test
(r_matrix[, cov_p, scale, invcov, …])Compute a Wald-test for a joint linear hypothesis. wald_test_terms
([skip_single, …])Compute a sequence of Wald tests for terms over multiple columns. Methods
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])Construct confidence interval for the fitted parameters. cov_params
([r_matrix, column, scale, cov_p, …])Compute the variance/covariance matrix. f_test
(r_matrix[, cov_p, scale, invcov])Compute the F-test for a joint linear hypothesis. get_bse
(**kwds)standard error of the parameter estimates with options initialize
(model, params, **kwargs)Initialize (possibly re-initialize) a Results instance. jtest
()overidentification test load
(fname)Load a pickled results instance normalized_cov_params
()See specific model class docstring predict
([exog, transform])Call self.model.predict with self.params as the first argument. remove_data
()Remove data arrays, all nobs arrays from result and model. save
(fname[, remove_data])Save a pickle of this instance. 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. t_test_pairwise
(term_name[, method, alpha, …])Perform pairwise t_test with multiple testing corrected p-values. wald_test
(r_matrix[, cov_p, scale, invcov, …])Compute a Wald-test for a joint linear hypothesis. wald_test_terms
([skip_single, …])Compute a sequence of Wald tests for terms over multiple columns. Properties
bse
The standard errors of the parameter estimates. bse_
standard error of the parameter estimates jval
nobs_moms attached by momcond_mean llf
Log-likelihood of model pvalues
The two-tailed p values for the t-stats of the params. q
Objective function at params tvalues
Return the t-statistic for a given parameter estimate. use_t