arXiv 24 Oct 2023 · Econometrics · publishedJournal of Business and Economic Statistics (2024) · 27 citations (OpenAlex)
arXiv:2310.15796 · PDF · DOI · OpenAlex · Extracted main text
The plausibility of the “parallel trends assumption” in Difference-in-Differences estimation is usually assessed by a test of the null hypothesis that the difference between the average outcomes of both groups is constant over time before the treatment. However, failure to reject the null hypothesis does not imply the absence of differences in time trends between both groups. We provide equivalence tests that allow researchers to find evidence in favor of the parallel trends assumption and thus increase the credibility of their treatment effect estimates. While we motivate our tests in the standard two-way fixed effects model, we discuss simple extensions to settings in which treatment adoption is staggered over time.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Berger, Roger L and Hsu, Jason C (1996) Bioequivalence trials, intersection-union tests and equivalence confidence sets | 0.928 | 4 | 3 | 100% |
| 2 | Hartman, Erin and Hidalgo, F. Daniel (2018) An Equivalence Approach to Balance and Placebo Tests | 0.928 | 4 | 3 | 100% |
| 3 | Wooldridge, Jeffrey M (2021) Two-way fixed effects, the two-way mundlak regression, and difference-in-differences estimators | 0.874 | 6 | 2 | 100% |
| 4 | Di Tella, Rafael and Schargrodsky, Ernesto (2004) Do Police Reduce Crime? Estimates Using the Allocation of Police Forces After a Terrorist Attack | 0.874 | 5 | 2 | 100% |
| 5 | Donohue, John J and Ho, D and Leahy, Patrick (2013) Do police reduce crime? A reexamination of a natural experiment | 0.737 | 3 | 2 | 100% |
| 6 | Ariella Kahn-Lang and Kevin Lang (2020) The Promise and Pitfalls of Differences-in-Differences: Reflections on 16 and Pregnant and Other Applications | 0.737 | 3 | 2 | 100% |
| 7 | Roth, Jonathan (2022) Pretest with Caution: Event-Study Estimates after Testing for Parallel Trends | 0.737 | 3 | 2 | 100% |
| 8 | Rambachan, Ashesh and Roth, Jonathan (2023) A More Credible Approach to Parallel Trends | 0.737 | 3 | 2 | 100% |
| 9 | Borusyak, Kirill and Jaravel, Xavier and Spiess, Jann (2023) Revisiting event study designs: Robust and efficient estimation | 0.644 | 2 | 2 | 100% |
| 10 | Fang, Zheng and Santos, Andres (2019) Inference on directionally differentiable functions | 0.585 | 3 | 1 | 100% |
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