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Trusting difference-in-difference estimates more: An approximate permutation test

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  • Sebastian Bunnenberg

    (Reutlingen University, ESB Business School)

Abstract

Researchers use difference-in-differences models to evaluate the causal effects of policy changes. Because the empirical correlation across firms and time can be ambiguous, estimating consistent standard errors is difficult, and statistical inferences may be biased. I 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 a specific parametric form on the residuals. In comparison with alternative parametric tests, this procedure maintains correct size with simulated and real-world interventions. Simultaneously, it improves power.

Suggested Citation

  • Sebastian Bunnenberg, 2021. "Trusting difference-in-difference estimates more: An approximate permutation test," 2021 Stata Conference 14, Stata Users Group.
  • Handle: RePEc:boc:scon21:14
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    File URL: http://fmwww.bc.edu/repec/scon2021/US21_Bunnenberg.pdf
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    References listed on IDEAS

    as
    1. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2011. "Robust Inference With Multiway Clustering," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(2), pages 238-249, April.
    2. Mitchell A. Petersen, 2009. "Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches," The Review of Financial Studies, Society for Financial Studies, vol. 22(1), pages 435-480, January.
    3. Marianne Bertrand & Esther Duflo & Sendhil Mullainathan, 2004. "How Much Should We Trust Differences-In-Differences Estimates?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 119(1), pages 249-275.
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