Ziyi Liu
arXiv 1 Sep 2025 · Econometrics
arXiv:2509.01829 · PDF · Extracted main text
This paper proposes a cohort-anchored framework for robust inference in event studies with staggered adoption, building on Rambachan and Roth (2023). Robust inference based on event-study coefficients aggregated across cohorts can be misleading due to the dynamic composition of treated cohorts, especially when pre-trends differ across cohorts. My approach avoids this problem by operating at the cohort-period level. To address the additional challenge posed by time-varying control groups in modern DiD estimators, I introduce the concept of block bias: the parallel-trends violation for a cohort relative to its fixed initial control group. I show that the biases of these estimators can be decomposed invertibly into block biases. Because block biases maintain a consistent comparison across pre- and post-treatment periods, researchers can impose transparent restrictions on them to conduct robust inference. In simulations and a reanalysis of minimum-wage effects on teen employment, my framework yields better-centered (and sometimes narrower) confidence sets than the aggregated approach when pre-trends vary across cohorts. The framework is most useful in settings with multiple cohorts, sufficient within-cohort precision, and substantial cross-cohort heterogeneity.
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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, A. and J. Roth (2023) A More Credible Approach to Parallel Trends | 1.000 | 28 | 4 | 100% |
| 2 | Callaway, B. and P. H. Sant’Anna (2021) Difference-in-Differences with Multiple Time Periods | 0.950 | 14 | 6 | 86% |
| 3 | Borusyak, K., X. Jaravel, and J. Spiess (2024) Revisiting event-study designs: robust and efficient estimation | 0.928 | 5 | 3 | 80% |
| 4 | Sun, L. and S. Abraham (2021) Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects | 0.874 | 5 | 2 | 100% |
| 5 | Liu, L., Y. Wang, and Y. Xu (2024) A practical guide to counterfactual estimators for causal inference with time-series cross-sectional data | 0.843 | 5 | 3 | 60% |
| 6 | Aguilar-Loyo, J (2025) A comparative analysis of two-way fixed effects estimators in staggered treatment designs | 0.811 | 4 | 2 | 100% |
| 7 | Andrews, I., J. Roth, and A. Pakes (2023) Inference for linear conditional moment inequalities | 0.737 | 3 | 2 | 100% |
| 8 | Roth, J (2024) Interpreting Event-Studies from Recent Difference-in-Differences Methods | 0.644 | 2 | 2 | 100% |
| 9 | Arkhangelsky, D. and A. Samkov (2024) Sequential synthetic difference in differences | 0.644 | 2 | 2 | 100% |
| 10 | De Chaisemartin, C. and X. d'Haultfoeuille (2024) Difference-in-Differences Estimators of Intertemporal Treatment Effects | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 19 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Interpreting Event-Studies from Recent Difference-in-Differences Methods | 0.644 | 4 | 1 |