Inference with Difference-in-Differences Revisited

Mike Brewer, Thomas Crossley () and Joyce Robert
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Joyce Robert: Institute for Fiscal Studies, London WC1E 7AE, UK

Journal of Econometric Methods, 2018, vol. 7, issue 1, 16

Abstract: A growing literature on inference in difference-in-differences (DiD) designs has been pessimistic about obtaining hypothesis tests of the correct size, particularly with few groups. We provide Monte Carlo evidence for four points: (i) it is possible to obtain tests of the correct size even with few groups, and in many settings very straightforward methods will achieve this; (ii) the main problem in DiD designs with grouped errors is instead low power to detect real effects; (iii) feasible GLS estimation combined with robust inference can increase power considerably whilst maintaining correct test size – again, even with few groups, and (iv) using OLS with robust inference can lead to a perverse relationship between power and panel length.

Keywords: cluster robust; difference in differences; feasible GLS; hypothesis test; power (search for similar items in EconPapers)
JEL-codes: C12 C13 C21 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (32)

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Working Paper: Inference with Difference-in-Differences Revisited (2013)
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DOI: 10.1515/jem-2017-0005

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