Effciency of Covariance Matrix Estimators for Maximum Likelihood Estimation
指出最大似然估计中常被忽略的条件信息矩阵方差估计量能达到半参数效率界，但计算困难；提出用模拟近似积分，并给出两种模拟方差估计量，蒙特卡洛结果证明其置信区间覆盖率优于标准方法。
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.