Inference in Differences-in-Differences with Few Treated Groups and Heteroskedasticity
The Review of Economics and Statistics
2019
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.
- DOI
- 10.1162/rest_a_00759
- Volume
- 101 (3)
- Pages
- 452-467
- Language
- en
- Export
- BibTeX
- Sources
- crossref openalex