statsmodels.sandbox.regression.gmm.IVRegressionResults

class statsmodels.sandbox.regression.gmm.IVRegressionResults(model, params, normalized_cov_params=None, scale=1.0, cov_type='nonrobust', cov_kwds=None, use_t=None, **kwargs)[source]

Results class for for an OLS model.

Most of the methods and attributes are inherited from RegressionResults. The special methods that are only available for OLS are:

  • get_influence

  • outlier_test

  • el_test

  • conf_int_el

See also

RegressionResults

Methods

HC0_se()

See statsmodels.RegressionResults

HC1_se()

See statsmodels.RegressionResults

HC2_se()

See statsmodels.RegressionResults

HC3_se()

See statsmodels.RegressionResults

aic()

bic()

bse()

centered_tss()

compare_f_test(restricted)

use F test to test whether restricted model is correct

compare_lm_test(restricted[, demean, use_lr])

Use Lagrange Multiplier test to test whether restricted model is correct

compare_lr_test(restricted[, large_sample])

Likelihood ratio test to test whether restricted model is correct

condition_number()

Return condition number of exogenous matrix.

conf_int([alpha, cols])

Returns the confidence interval of the fitted parameters.

cov_HC0()

See statsmodels.RegressionResults

cov_HC1()

See statsmodels.RegressionResults

cov_HC2()

See statsmodels.RegressionResults

cov_HC3()

See statsmodels.RegressionResults

cov_params([r_matrix, column, scale, cov_p, …])

Returns the variance/covariance matrix.

eigenvals()

Return eigenvalues sorted in decreasing order.

ess()

f_pvalue()

f_test(r_matrix[, cov_p, scale, invcov])

Compute the F-test for a joint linear hypothesis.

fittedvalues()

fvalue()

get_prediction([exog, transform, weights, …])

compute prediction results

get_robustcov_results([cov_type, use_t])

create new results instance with robust covariance as default

initialize(model, params, **kwd)

llf()

load(fname)

load a pickle, (class method)

mse_model()

mse_resid()

mse_total()

nobs()

normalized_cov_params()

predict([exog, transform])

Call self.model.predict with self.params as the first argument.

pvalues()

remove_data()

remove data arrays, all nobs arrays from result and model

resid()

resid_pearson()

Residuals, normalized to have unit variance.

rsquared()

rsquared_adj()

save(fname[, remove_data])

save a pickle of this instance

scale()

spec_hausman([dof])

Hausman’s specification test

ssr()

summary([yname, xname, title, alpha])

Summarize the Regression Results

summary2([yname, xname, title, alpha, …])

Experimental summary function to 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.

uncentered_tss()

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

wresid()

Attributes

use_t