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
- Attributes
- HC0_se
White’s (1980) heteroskedasticity robust standard errors.
Defined as sqrt(diag(X.T X)^(-1)X.T diag(e_i^(2)) X(X.T X)^(-1) where e_i = resid[i].
When HC0_se or cov_HC0 is called the RegressionResults instance will then have another attribute het_scale, which is in this case is just resid**2.
- HC1_se
MacKinnon and White’s (1985) heteroskedasticity robust standard errors.
Defined as sqrt(diag(n/(n-p)*HC_0).
When HC1_se or cov_HC1 is called the RegressionResults instance will then have another attribute het_scale, which is in this case is n/(n-p)*resid**2.
- HC2_se
MacKinnon and White’s (1985) heteroskedasticity robust standard errors.
Defined as (X.T X)^(-1)X.T diag(e_i^(2)/(1-h_ii)) X(X.T X)^(-1) where h_ii = x_i(X.T X)^(-1)x_i.T
When HC2_se or cov_HC2 is called the RegressionResults instance will then have another attribute het_scale, which is in this case is resid^(2)/(1-h_ii).
- HC3_se
MacKinnon and White’s (1985) heteroskedasticity robust standard errors.
Defined as (X.T X)^(-1)X.T diag(e_i^(2)/(1-h_ii)^(2)) X(X.T X)^(-1) where h_ii = x_i(X.T X)^(-1)x_i.T.
When HC3_se or cov_HC3 is called the RegressionResults instance will then have another attribute het_scale, which is in this case is resid^(2)/(1-h_ii)^(2).
- aic
Akaike’s information criteria.
For a model with a constant \(-2llf + 2(df\_model + 1)\). For a model without a constant \(-2llf + 2(df\_model)\).
- bic
Bayes’ information criteria.
For a model with a constant \(-2llf + \log(n)(df\_model+1)\). For a model without a constant \(-2llf + \log(n)(df\_model)\).
- 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.
Calculated as ratio of largest to smallest eigenvalue.
- cov_HC0
Heteroscedasticity robust covariance matrix. See HC0_se.
- cov_HC1
Heteroscedasticity robust covariance matrix. See HC1_se.
- cov_HC2
Heteroscedasticity robust covariance matrix. See HC2_se.
- cov_HC3
Heteroscedasticity robust covariance matrix. See HC3_se.
- eigenvals
Return eigenvalues sorted in decreasing order.
- ess
The explained sum of squares.
If a constant is present, the centered total sum of squares minus the sum of squared residuals. If there is no constant, the uncentered total sum of squares is used.
- 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.
The explained sum of squares divided by the model degrees of freedom.
- mse_resid
Mean squared error of the residuals.
The sum of squared residuals divided by the residual degrees of freedom.
- mse_total
Total mean squared error.
The uncentered total sum of squares divided by the number of observations.
- 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.
- array_like
The array wresid normalized by the sqrt of the scale to have unit variance.
- rsquared
R-squared of the model.
This is defined here as 1 - ssr/centered_tss if the constant is included in the model and 1 - ssr/uncentered_tss if the constant is omitted.
- rsquared_adj
Adjusted R-squared.
This is defined here as 1 - (nobs-1)/df_resid * (1-rsquared) if a constant is included and 1 - nobs/df_resid * (1-rsquared) if no constant is included.
- ssr
Sum of squared (whitened) residuals.
- tvalues
Return the t-statistic for a given parameter estimate.
- uncentered_tss
Uncentered sum of squares.
The sum of the squared values of the (whitened) endogenous response variable.
use_tFlag indicating to use the Student’s distribution in inference.
- wresid
The residuals of the transformed/whitened regressand and regressor(s).
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
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.
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.
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
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.
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
White’s (1980) heteroskedasticity robust standard errors.
MacKinnon and White’s (1985) heteroskedasticity robust standard errors.
MacKinnon and White’s (1985) heteroskedasticity robust standard errors.
MacKinnon and White’s (1985) heteroskedasticity robust standard errors.
Akaike’s information criteria.
Bayes’ information criteria.
The standard errors of the parameter estimates.
The total (weighted) sum of squares centered about the mean.
Return condition number of exogenous matrix.
Heteroscedasticity robust covariance matrix.
Heteroscedasticity robust covariance matrix.
Heteroscedasticity robust covariance matrix.
Heteroscedasticity robust covariance matrix.
Return eigenvalues sorted in decreasing order.
The explained sum of squares.
The p-value of the F-statistic.
The predicted values for the original (unwhitened) design.
Log-likelihood of model
Mean squared error the model.
Mean squared error of the residuals.
Total mean squared error.
Number of observations n.
The two-tailed p values for the t-stats of the params.
The residuals of the model.
Residuals, normalized to have unit variance.
R-squared of the model.
Adjusted R-squared.
Sum of squared (whitened) residuals.
Return the t-statistic for a given parameter estimate.
Uncentered sum of squares.
Flag indicating to use the Student’s distribution in inference.
The residuals of the transformed/whitened regressand and regressor(s).