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)

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

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
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.
scale()	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.

Properties

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).