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.
Authors
Mike Brewer
Research Fellow University of Michigan
Tom is a Research Fellow at IFS, a Research Professor for the Institute for Social Research at the University of Michigan.
Deputy Director
Robert is a Deputy Director. His work focuses on primarily on the labour market, income and wealth inequality, and the design of the welfare system.
Journal article details
- DOI
- 10.1515/jem-2017-0005
- Publisher
- De Gruyter
- JEL
- C12; C13; C21
- Issue
- October 2017
Suggested citation
M, Brewer and T, Crossley and R, Joyce. (2017). 'Inference with difference-in-differences revisited' (2017)
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