Sunny R. Karim, Morten Ørregaard Nielsen, James G. MacKinnon, Matthew D. Webb
arXiv 12 Feb 2026 · Econometrics
arXiv:2602.12043 · PDF · DOI · OpenAlex · Extracted main text
Obtaining reliable inferences with traditional difference-in-differences (DiD) methods can be difficult. Problems can arise when both outcomes and errors are serially correlated, when there are few clusters or few treated clusters, when cluster sizes vary greatly, and in various other cases. In recent years, recognition of the “staggered adoption” problem has shifted the focus away from inference towards consistent estimation of treatment effects. One of the most popular new estimators is the CSDID procedure of Callaway and Sant'Anna (2021). We find that the issues of over-rejection with few clusters and/or few treated clusters are at least as severe for CSDID as for traditional DiD methods. We also propose using a cluster jackknife for inference with CSDID, which simulations suggest greatly improves inference. We provide software packages in Stata csdidjack and R didjack to calculate cluster-jackknife standard errors easily.
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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. Sant’Anna (2021) Difference-in-differences with multiple time periods | 1.000 | 17 | 8 | 100% |
| 2 | Mac\-Kinnon, J. G., M. . Nielsen, and M. D. Webb (2023) Fast and reliable jackknife and bootstrap methods for cluster-robust inference self | 0.928 | 4 | 4 | 100% |
| 3 | Weiss, A (2024) How much should we trust modern difference-in-differences estimates? | 0.928 | 4 | 3 | 100% |
| 4 | Mac\-Kinnon, J. G., M. . Nielsen, and M. D. Webb (2023) Cluster-robust inference: A guide to empirical practice self | 0.843 | 3 | 3 | 100% |
| 5 | De Chaisemartin, C. and X. d’Haultfoeuille (2020) Two-way fixed effects estimators with heterogeneous treatment effects | 0.843 | 3 | 3 | 100% |
| 6 | Mizushima, Y. and D. Powell (2025) Inference with modern difference-in-differences methods | 0.843 | 3 | 3 | 100% |
| 7 | Goodman-Bacon, A (2021) Difference-in-differences with variation in treatment timing | 0.737 | 3 | 2 | 100% |
| 8 | Bertrand, M., E. Duflo, and S. Mullainathan (2004) How much should we trust differences-in-differences estimates? | 0.644 | 2 | 2 | 100% |
| 9 | Cameron, A. C., J. B. Gelbach, and D. L. Miller (2008) Bootstrap-based improvements for inference with clustered errors | 0.644 | 2 | 2 | 100% |
| 10 | Hansen, B. E (2025) Standard errors for difference-in-difference regression | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 25 scored citations.