Inference with Difference-in-Differences Revisited

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

No 7742, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: A growing literature on inference in difference-in-differences (DiD) designs with grouped errors has been pessimistic about obtaining hypothesis tests of the correct size, particularly with few groups. We provide Monte Carlo evidence for three 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; and (iii) feasible GLS estimation combined with robust inference can increase power considerably whilst maintaining correct test size – again, even with few groups.

Keywords: difference in differences; hypothesis test; power; cluster robust; feasible GLS (search for similar items in EconPapers)
JEL-codes: C12 C13 C21 (search for similar items in EconPapers)
Pages: 38 pages
Date: 2013-11
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (49)

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Journal Article: Inference with Difference-in-Differences Revisited (2018)
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