Trusting Difference-in-Differences Estimates More: An Approximate Permutation Test

30 Pages Posted: 6 Jul 2016 Last revised: 16 Mar 2018

See all articles by Sebastian Bunnenberg

Sebastian Bunnenberg

Reutlingen University - ESB Business School; Talanx AG - HDI Global SE

Steffen Meyer

University of Southern Denmark - Department of Business and Economics; Danish Finance Institute

Date Written: February 22, 2017

Abstract

Researchers use difference-in-differences models to evaluate the causal effects of policy changes. As the empirical correlation across firms and time can be ambiguous, estimating consistent standard errors is difficult and statistical inferences may be biased. We apply an approximate permutation test using simulated interventions to reveal the empirical error distribution of estimated policy effects. In contrast to existing econometric corrections, such as single- or double-clustering, this approach does not impose any parametric form on the data. In comparison with alternative parametric tests, this procedure maintains correct size with simulated and real-world interventions. Simultaneously, it improves power.

Keywords: difference-in-differences, policy studies, clustered standard errors, placebo laws, approximate permutation tests

JEL Classification: C12, C14, C93

Suggested Citation

Bunnenberg, Sebastian and Meyer, Steffen, Trusting Difference-in-Differences Estimates More: An Approximate Permutation Test (February 22, 2017). Available at SSRN: https://ssrn.com/abstract=2805116 or http://dx.doi.org/10.2139/ssrn.2805116

Sebastian Bunnenberg (Contact Author)

Reutlingen University - ESB Business School ( email )

Alteburgstr. 150
Reutlingen, 72762
Germany

Talanx AG - HDI Global SE ( email )

HDI-Platz 1
Hannover, 30659
Germany

Steffen Meyer

University of Southern Denmark - Department of Business and Economics ( email )

DK-5230 Odense
Denmark

Danish Finance Institute ( email )

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