statsmodels.sandbox.regression.gmm.IVGMMResults¶
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class
statsmodels.sandbox.regression.gmm.
IVGMMResults
(*args, **kwds)[source]¶ Results class of IVGMM
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
See specific model class docstring
predict
([exog, transform])Call self.model.predict with self.params as the first argument.
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
The standard errors of the parameter estimates.
standard error of the parameter estimates
Fitted values
nobs_moms attached by momcond_mean
Log-likelihood of model
The two-tailed p values for the t-stats of the params.
Objective function at params
Residuals
Sum of square errors
Return the t-statistic for a given parameter estimate.