statsmodels.sandbox.regression.gmm.GMMResults

class statsmodels.sandbox.regression.gmm.GMMResults(*args, **kwds) [source]

just a storage class right now

Methods

bse()
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([r_matrix, column, scale, cov_p, …])	Returns 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, **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
t_test_pairwise(term_name[, method, alpha, …])	perform pairwise t_test with multiple testing corrected p-values
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.
wald_test_terms([skip_single, …])	Compute a sequence of Wald tests for terms over multiple columns

Attributes

bse_	standard error of the parameter estimates
use_t