statsmodels.sandbox.regression.gmm.GMMResults
-
class statsmodels.sandbox.regression.gmm.GMMResults(*args, **kwds)
[source] -
just a storage class right now
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, 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. 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 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 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
bse
standard error of the parameter estimates use_t
Please login to continue.