statsmodels.sandbox.regression.gmm.IVGMMResults

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

Results class of IVGMM

Attributes

bse_	standard error of the parameter estimates

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
normalized_cov_params()	See specific model class docstring
predict([exog, transform])	Call self.model.predict with self.params as the first argument.
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.
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.

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
normalized_cov_params()	See specific model class docstring
predict([exog, transform])	Call self.model.predict with self.params as the first argument.
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.
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

bse	The standard errors of the parameter estimates.
bse_	standard error of the parameter estimates
fittedvalues	Fitted values
jval	nobs_moms attached by momcond_mean
llf	Log-likelihood of model
pvalues	The two-tailed p values for the t-stats of the params.
q	Objective function at params
resid	Residuals
ssr	Sum of square errors
tvalues	Return the t-statistic for a given parameter estimate.
use_t