6.7.4.4.7. statsmodels.sandbox.regression.gmm.IVRegressionResults¶
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class
statsmodels.sandbox.regression.gmm.
IVRegressionResults
(model, params, normalized_cov_params=None, scale=1.0, cov_type='nonrobust', cov_kwds=None, use_t=None)[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
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__init__
(model, params, normalized_cov_params=None, scale=1.0, cov_type='nonrobust', cov_kwds=None, use_t=None)¶
6.7.4.4.7.1. Methods¶
HC0_se () |
See statsmodels.RegressionResults |
HC1_se () |
See statsmodels.RegressionResults |
HC2_se () |
See statsmodels.RegressionResults |
HC3_se () |
See statsmodels.RegressionResults |
__init__ (model, params[, ...]) |
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aic () |
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bic () |
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bse () |
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centered_tss () |
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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 () |
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f_pvalue () |
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f_test (r_matrix[, cov_p, scale, invcov]) |
Compute the F-test for a joint linear hypothesis. |
fittedvalues () |
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fvalue () |
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get_robustcov_results ([cov_type, use_t]) |
create new results instance with robust covariance as default |
initialize (model, params, **kwd) |
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llf () |
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load (fname) |
load a pickle, (class method) |
mse_model () |
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mse_resid () |
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mse_total () |
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nobs () |
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normalized_cov_params () |
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predict ([exog, transform]) |
Call self.model.predict with self.params as the first argument. |
pvalues () |
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remove_data () |
remove data arrays, all nobs arrays from result and model |
resid () |
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resid_pearson () |
Residuals, normalized to have unit variance. |
rsquared () |
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rsquared_adj () |
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save (fname[, remove_data]) |
save a pickle of this instance |
scale () |
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spec_hausman ([dof]) |
Hausman’s specification test |
ssr () |
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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 () |
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wald_test (r_matrix[, cov_p, scale, invcov, ...]) |
Compute a Wald-test for a joint linear hypothesis. |
wresid () |