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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 bool(x) -> bool

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