Inference in Differences-in-Differences with Few Treated Groups and Heteroskedasticity
Bruno Ferman and
Cristine Pinto ()
The Review of Economics and Statistics, 2019, vol. 101, issue 3, 452-467
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.
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Working Paper: Inference in differences-in-differences with few treated groups and heteroskedasticity (2015)
Working Paper: Inference in Differences-in-Differences with Few Treated Groups and Heteroskedasticity (2015)
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