arXiv 14 Jan 2026 · Econometrics
arXiv:2601.18801 · PDF · DOI · OpenAlex · Extracted main text
This paper develops a design-first econometric framework for event-study and difference-in-differences estimands under staggered adoption with heterogeneous effects, emphasising (i) exact probability limits for conventional two-way fixed effects event-study regressions, (ii) computable design diagnostics that quantify contamination and negative-weight risk, and (iii) sensitivity-robust inference that remains uniformly valid under restricted violations of parallel trends. The approach is accompanied by orthogonal score constructions that reduce bias from high-dimensional nuisance estimation when conditioning on covariates. Theoretical results and Monte Carlo experiments jointly deliver a self-contained methodology paper suitable for finance and econometrics applications where timing variation is intrinsic to policy, regulation, and market-structure changes.
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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 | Callaway, B. and P. H. C. Sant'Anna (2021) Difference-in-Differences with Multiple Time Periods | 1.000 | 13 | 3 | 100% |
| 2 | Chernozhukov, V., W. K. Newey, and R. Singh (2022) Automatic Debiased Machine Learning of Causal and Structural Effects | 1.000 | 9 | 3 | 100% |
| 3 | Masten, M. A. and A. Poirier (2021) Salvaging Falsified Instrumental Variable Models | 1.000 | 5 | 4 | 100% |
| 4 | Sun, L. and S. Abraham (2021) Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects | 0.984 | 21 | 7 | 95% |
| 5 | de Chaisemartin, C. and X. D'Haultfuille (2023) Two-way Fixed Effects and Differences-in-Differences with Heterogeneous Treatment Effects: A Survey | 0.983 | 20 | 8 | 95% |
| 6 | Rambachan, A. and J. Roth (2023) A More Credible Approach to Parallel Trends | 0.971 | 12 | 5 | 92% |
| 7 | Wing, C., M. Yozwiak, A. Hollingsworth, S. Freedman, and K. Simon (2024) Designing Difference-in-Difference Studies with Staggered Treatment Adoption: Key Concepts and Practical Guidelines | 0.874 | 16 | 2 | 100% |
| 8 | Roth, J (2024) Interpreting Event-Studies from Recent Difference-in-Differences Methods | 0.874 | 11 | 2 | 100% |
| 9 | Abadie, A., J. Angrist, and B. Frandsen (2025) Harvesting Differences-in-Differences and Event-Study Evidence, Working Paper 34550, National Bureau of Economic Research | 0.874 | 10 | 2 | 100% |
| 10 | Sant'Anna, P. H. C. and J. Zhao (2025) Efficient Difference-in-Differences and Event Study Estimators, Tech | 0.874 | 5 | 2 | 100% |
Showing the top 10 of 23 scored citations.