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 : ndarray
mean of parameter estimates over bootstrap replications
std : ndarray
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.