Inference with Difference-in-Differences Revisited

By M. Brewer, Thomas F Crossley, Robert A. Joyce. Social Science Research Network, 2013

110 citations recorded by the source corpus.

Abstract 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.

Subfields: Economics and Econometrics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3efe70d38038de2d126a850c9ab79c8d07ca0730

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Citations in the corpus

Regression Analysis of Country Effects Using Multilevel Data: A Cautionary Tale (2013, methodology): “Recent studies have indicated that a simple rescaling of the cluster-robust standard errors, and the use of critical values from the t-distribution, may deliver reliable test results (Bester et al 2011, Brewer et al 2013).”
Cluster-robust inference: A guide to empirical practice (2022, methodology): “Canay, Romano and Shaikh (2017) proposes “approximate randomization tests” based on the cluster-level estimators of Ibragimov and Müller (2010); see Cai, Canay, Kim and Shaikh (2021) for a guide to this approach.”
Is Modern Technology Responsible for Jobless Recoveries? (2017, methodology): “We cluster standard errors by country, using the small-group adjustment that Stata implements by default (Brewer, Crossley, and Joyce, 2013).”