A Monte Carlo Analysis of Alternative Estimators in Models Involving Selectivity
用蒙特卡洛和响应面技术，评估了五种常用于处理内生二元选择性决策的估计量，发现带虚拟变量的最小二乘法表现接近完全信息最大似然法，而两阶段估计量稳健性严重不足。
In a simultaneous-equation model involving selectivity, Monte Carlo and response-surface techniques are used to assess the performance of five estimators commonly applied to a behavioral equation conditioned on an endogenous binary selectivity decision. The estimators include least squares with an exogenous dummy variable for the selectivity decision, three two-stage estimators that employ the estimated probability of the selectivity decision, and full information maximum likelihood (FIML). Although formally inconsistent, least squares with dummy variables is found to perform nearly as well as FIML, based on mean squared error measures. All two-stage estimators are found to be seriously deficient in terms of robustness.