When Can We Use Two-Way Fixed-Effects (TWFE): A Comparison of TWFE and Novel Dynamic Difference-in-Differences Estimators
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何时可以使用双向固定效应：与新型动态双重差分估计量的比较
- 研究问题
- 在处理效应异质或关键识别假设被违反时，传统双向固定效应与新型动态双重差分估计量分别会出现哪些问题和适用边界？
- 方法与思路
- 作者解释 TWFE 的问题与替代估计量特征，展示其不一致条件，并用 Monte Carlo 模拟比较假设违背下的动态效应估计表现。
- 材料陈述的贡献
- 摘要称，指定事件时间函数时 TWFE 通常表现较好；新方法能处理异质效应问题，但各方法仍对非平行趋势、预期效应及外生性违背敏感。
- 阅读关注点
- 应核对事件时间函数的具体设定、处理批次和效应异质性设计，以及模拟如何违反各项假设、各估计量比较的目标是否一致。
- 阅读边界
- 这是预印本，同行评审及期刊接收未核实；摘要未说明模拟参数与误差数值，“表现较好”及共同敏感性不能直接视为一般定理。
原文摘要
The conventional Two-Way Fixed-Effects (TWFE) estimator has come under scrutiny lately. Recent literature has revealed potential shortcomings of TWFE when the treatment effects are heterogeneous. Scholars have developed new advanced dynamic Difference-in-Differences (DiD) estimators to tackle these potential shortcomings. However, confusion remains in applied research as to when the conventional TWFE is biased and what issues the novel estimators can and cannot address. In this study, we first provide an intuitive explanation of the problems of TWFE and elucidate the key features of the novel alternative DiD estimators. We then systematically demonstrate the conditions under which the conventional TWFE is inconsistent. We employ Monte Carlo simulations to assess the performance of dynamic DiD estimators under violations of key assumptions, which likely happens in applied cases. While the new dynamic DiD estimators offer notable advantages in capturing heterogeneous treatment effects, we show that the conventional TWFE performs generally well if the model specifies an event-time function. All estimators are equally sensitive to violations of the parallel trends assumption, anticipation effects or violations of time-varying exogeneity. Despite their advantages, the new dynamic DiD estimators tackle a very specific problem and they do not serve as a universal remedy for violations of the most critical assumptions. We finally derive, based on our simulations, recommendations for how and when to use TWFE and the new DiD estimators in applied research.