# 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

RegressionResults
 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 a set of linear restrictions. compare_lr_test(restricted[, large_sample]) Likelihood ratio test to test whether restricted model is correct. conf_int([alpha, cols]) Compute the confidence interval of 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_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, **kwargs) Initialize (possibly re-initialize) a Results instance. 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. A scale factor for the covariance matrix. spec_hausman([dof]) Hausman’s specification test 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. 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.
 HC0_se White’s (1980) heteroskedasticity robust standard errors. HC1_se MacKinnon and White’s (1985) heteroskedasticity robust standard errors. HC2_se MacKinnon and White’s (1985) heteroskedasticity robust standard errors. HC3_se MacKinnon and White’s (1985) heteroskedasticity robust standard errors. aic Akaike’s information criteria. bic Bayes’ information criteria. bse The standard errors of the parameter estimates. centered_tss The total (weighted) sum of squares centered about the mean. condition_number Return condition number of exogenous matrix. cov_HC0 Heteroscedasticity robust covariance matrix. cov_HC1 Heteroscedasticity robust covariance matrix. cov_HC2 Heteroscedasticity robust covariance matrix. cov_HC3 Heteroscedasticity robust covariance matrix. eigenvals Return eigenvalues sorted in decreasing order. ess The explained sum of squares. f_pvalue The p-value of the F-statistic. fittedvalues The predicted values for the original (unwhitened) design. fvalue llf Log-likelihood of model mse_model Mean squared error the model. mse_resid Mean squared error of the residuals. mse_total Total mean squared error. nobs Number of observations n. pvalues The two-tailed p values for the t-stats of the params. resid The residuals of the model. resid_pearson Residuals, normalized to have unit variance. rsquared R-squared of the model. rsquared_adj Adjusted R-squared. ssr Sum of squared (whitened) residuals. tvalues Return the t-statistic for a given parameter estimate. uncentered_tss Uncentered sum of squares. use_t Flag indicating to use the Student’s distribution in inference. wresid The residuals of the transformed/whitened regressand and regressor(s).