Inference in Differences-in-Differences with Few Treated Groups and Heteroskedasticity
提出一种在双重差分设定中处理组少、对照组多且存在异方差时仍有效的推断方法，并证明组规模差异导致的异方差会使现有方法失效。
We derive an inference method that works in differences-in-differences settings with few treated and many control groups in the presence of heteroskedasticity. As a leading example, we provide theoretical justification and empirical evidence that heteroskedasticity generated by variation in group sizes can invalidate existing inference methods, even in data sets with a large number of observations per group. In contrast, our inference method remains valid in this case. Our test can also be combined with feasible generalized least squares, providing a safeguard against misspecification of the serial correlation.