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

HC0_se()	See statsmodels.RegressionResults
HC1_se()	See statsmodels.RegressionResults
HC2_se()	See statsmodels.RegressionResults
HC3_se()	See statsmodels.RegressionResults
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.
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()	Explained sum of squares.
f_pvalue()	p-value of the F-statistic
f_test(r_matrix[, cov_p, scale, invcov])	Compute the F-test for a joint linear hypothesis.
fittedvalues()	The predicted values for the original (unwhitened) design.
fvalue()	F-statistic of the fully specified model.
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)	Initialize (possibly re-initialize) a Results instance.
llf()	Log-likelihood of model
load(fname)	load a pickle, (class method)
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.
normalized_cov_params()	See specific model class docstring
predict([exog, transform])	Call self.model.predict with self.params as the first argument.
pvalues()	The two-tailed p values for the t-stats of the params.
remove_data()	remove data arrays, all nobs arrays from result and model
resid()	The residuals of the model.
resid_pearson()	Residuals, normalized to have unit variance.
rsquared()	R-squared of a model with an intercept.
rsquared_adj()	Adjusted R-squared.
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
ssr()	Sum of squared (whitened) residuals.
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()	Uncentered sum of squares.
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()	The residuals of the transformed/whitened regressand and regressor(s)