Simplified Implementation of the Heckman Estimator of the Dynamic Probit Model and a Comparison with Alternative Estimators*
提出一种用标准软件实现动态随机效应Probit模型Heckman估计量的简便方法，并在失业概率实证和模拟实验中比较Heckman、Orme和Wooldridge三种估计量，发现无一种在所有情况下占优，但多数情况下表现良好，除非时间期数很少。
Abstract This paper presents a convenient shortcut method for implementing the Heckman estimator of the dynamic random effects probit model and other dynamic nonlinear panel data models using standard software. It then compares the estimators proposed by Heckman, Orme and Wooldridge, based on three alternative approximations, first in an empirical model for the probability of unemployment and then in a set of simulation experiments. The results indicate that none of the three estimators dominates the other two in all cases. In most cases, all three estimators display satisfactory performance, except when the number of time periods is very small.