Estimating heterogeneous effects in static binary response panel data models
研究了当结果为二元变量时，面板数据模型中异质性效应的估计问题，指出按子样本分别估计会导致不一致，并提出了考虑非随机分类的估计方法，通过蒙特卡洛模拟和性别与职业满意度差异的实证应用验证了方法有效性。
.This article considers estimating heterogeneous effects in panel data models when the outcome is binary. We argue that a common practice of splitting the sample and performing estimation separately for each subsample results in inconsistent estimators of heterogeneous parameters. The article presents methods that account for a possibility of nonrandom sorting and produce consistent estimators of causal effects in two or more heterogeneous sub-populations. Monte Carlo simulations show that considered methods perform well in finite samples. As an empirical application, the article studies gender differences in job satisfaction by occupation type.