%0 Report
        %A Brewer, Mike
%A Crossley, Thomas F.
%A Joyce, Robert
        %T Inference with Difference-in-Differences Revisited
        %D 2013
        %8 2013 Nov
        %I Institute of Labor Economics (IZA)
        %C Bonn
        %7 IZA Discussion Paper
        %N 7742
        %U https://www.iza.org/index.php/publications/dp7742
        %X 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.
        %K difference in differences
%K hypothesis test
%K power
%K cluster robust
%K feasible GLS