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