Inference with Difference-in-Differences Revisited - Mike Brewer; Thomas F. Crossley; Robert Joyce (Institute for the Study of Labor (IZA), 2013)
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: C12; C13; C21; 330; difference in differences; hypothesis test; power; cluster robust; feasible GLS