arXiv 22 Jan 2024 · Econometrics · 36 citations (OpenAlex)
arXiv:2401.12309 · PDF · DOI · OpenAlex · Extracted main text
This note discusses the interpretation of event-study plots produced by recent difference-in-differences methods. I show that even when specialized to the case of non-staggered treatment timing, the default plots produced by software for three of the most popular recent methods (de Chaisemartin and D'Haultfoeuille, 2020; Callaway and SantAnna, 2021; Borusyak, Jaravel and Spiess, 2024) do not match those of traditional two-way fixed effects (TWFE) event-studies: the new methods may show a kink or jump at the time of treatment even when the TWFE event-study shows a straight line. This difference stems from the fact that the new methods construct the pre-treatment coefficients asymmetrically from the post-treatment coefficients. As a result, visual heuristics for analyzing TWFE event-study plots should not be immediately applied to those from these methods. I conclude with practical recommendations for constructing and interpreting event-study plots when using these methods.
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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 | Rambachan, Ashesh and Roth, Jonathan (2023) A More Credible Approach to Parallel Trends self | 0.811 | 4 | 2 | 100% |
| 2 | Liu, Ziyi (2025) Cohort-Anchored Robust Inference for Event-Study with Staggered Adoption | 0.644 | 4 | 1 | 100% |
| 3 | de Chaisemartin, Clément and D'Haultfœuille, Xavier (2020) Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects | 0.644 | 2 | 2 | 100% |
| 4 | Gardner, John (2021) Two-stage differences in differences | 0.644 | 2 | 2 | 100% |
| 5 | Liu, Licheng and Wang, Ye and Xu, Yiqing (2024) A Practical Guide to Counterfactual Estimators for Causal Inference with Time-Series Cross-Sectional Data | 0.644 | 2 | 2 | 100% |
| 6 | Roth, Jonathan (2022) Pre-test with Caution: Event-study Estimates After Testing for Parallel Trends self | 0.644 | 2 | 2 | 100% |
| 7 | Sun, Liyang and Abraham, Sarah (2021) Estimating dynamic treatment effects in event studies with heterogeneous treatment effects | 0.644 | 2 | 2 | 100% |
| 8 | Li, Zikai and Strezhnev, Anton (2025) Benchmarking parallel trends violations in regression imputation difference-in-differences | 0.511 | 3 | 2 | 33% |
| 9 | Bilinski, Alyssa and Hatfield, Laura A (2018) Seeking evidence of absence: Reconsidering tests of model assumptions | 0.405 | 1 | 1 | 100% |
| 10 | Borusyak, Kirill and Jaravel, Xavier and Spiess, Jann (2024) Revisiting Event-Study Designs: Robust and Efficient Estimation | 0.405 | 1 | 1 | 100% |
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