statsmodels.sandbox.regression.gmm.IVGMMResults

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

Results class of IVGMM

Attributes
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.

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, 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, 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, invcov, use_f, ...])

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