statsmodels.base.model.GenericLikelihoodModelResults.bootstrap

GenericLikelihoodModelResults.bootstrap(nrep=100, method='nm', disp=0, store=1)

simple bootstrap to get mean and variance of estimator

see notes

Parameters:

• nrep (int) – number of bootstrap replications
• method (str) – optimization method to use
• disp (bool) – If true, then optimization prints results
• store (bool) – If true, then parameter estimates for all bootstrap iterations are attached in self.bootstrap_results

Returns:

• mean (array) – mean of parameter estimates over bootstrap replications
• std (array) – standard deviation of parameter estimates over bootstrap replications

Notes

This was mainly written to compare estimators of the standard errors of the parameter estimates. It uses independent random sampling from the original endog and exog, and therefore is only correct if observations are independently distributed.

This will be moved to apply only to models with independently distributed observations.