Inference with difference-in-differences revisited
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.
Journal of Econometric Methods
University of Essex Research Repository - http://repository.essex.ac.uk/id/eprint/20372
Open Access; ©2018 Thomas F. Crossley et al., published by De Gruyter. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0