statsmodels.sandbox.regression.gmm.IVGMMResults

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

Results class of IVGMM

Attributes

bse_ standard error of the parameter estimates

Methods

bse() The standard errors of the parameter estimates.
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.
fittedvalues() Fitted values
get_bse(**kwds) standard error of the parameter estimates with options
initialize(model, params, **kwd) Initialize (possibly re-initialize) a Results instance.
jtest() overidentification test
jval() nobs_moms attached by momcond_mean
llf() Log-likelihood of model
load(fname) load a pickle, (class method); use only on trusted files, as unpickling can run arbitrary code.
normalized_cov_params() See specific model class docstring
predict([exog, transform]) Call self.model.predict with self.params as the first argument.
pvalues() The two-tailed p values for the t-stats of the params.
q() Objective function at params
remove_data() remove data arrays, all nobs arrays from result and model
resid() Residuals
save(fname[, remove_data]) save a pickle of this instance
ssr() Sum of square errors
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