Efficiency of Covariance Matrix Estimators for Maximum Likelihood Estimation.

When econometric models are estimated by maximum likelihood, the conditional information matrix variance estimator is usually avoided in choosing a method for estimating the variance of the parameter estimate. However, the conditional information matrix estimator attains the semiparametric efficiency bound for the variance estimation problem. Unfortunately, for even moderately complex models, the integral involved in computation of the conditional information matrix estimator is prohibitively difficult to solve. Simulation is suggested to approximate the integral, and two simulation variance estimators are proposed. Monte Carlo results suggest these estimators are attractive in providing accurate confidence interval coverage rates compared to the standard maximum likelihood variance estimators.