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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

Abstract: 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.

Date: 2019
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Related works:
Working Paper: Inference in differences-in-differences with few treated groups and heteroskedasticity (2015) Downloads
Working Paper: Inference in Differences-in-Differences with Few Treated Groups and Heteroskedasticity (2015) Downloads
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The Review of Economics and Statistics is currently edited by Amitabh Chandra, Olivier Coibion, Bryan S. Graham, Shachar Kariv, Amit K. Khandelwal, Asim Ijaz Khwaja, Brigitte C. Madrian and Rohini Pande

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