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
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()

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
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