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Improved Inference for CSDID Using the Cluster Jackknife

Author

Listed:
  • Sunny R. Karim
  • Morten {O}rregaard Nielsen
  • James G. MacKinnon
  • Matthew D. Webb

Abstract

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.

Suggested Citation

  • Sunny R. Karim & Morten {O}rregaard Nielsen & James G. MacKinnon & Matthew D. Webb, 2026. "Improved Inference for CSDID Using the Cluster Jackknife," Papers 2602.12043, arXiv.org.
    • Handle: RePEc:arx:papers:2602.12043
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    References listed on IDEAS

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    1. Partha Deb & Edward C. Norton & Jeffrey M. Wooldridge & Jeffrey E. Zabel, 2024. "A Flexible, Heterogeneous Treatment Effects Difference-in-Differences Estimator for Repeated Cross-Sections," NBER Working Papers 33026, National Bureau of Economic Research, Inc.
    2. Goodman-Bacon, Andrew, 2021. "Difference-in-differences with variation in treatment timing," Journal of Econometrics, Elsevier, vol. 225(2), pages 254-277.
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    5. Djogbenou, Antoine A. & MacKinnon, James G. & Nielsen, Morten Ørregaard, 2019. "Asymptotic theory and wild bootstrap inference with clustered errors," Journal of Econometrics, Elsevier, vol. 212(2), pages 393-412.
    6. Kirill Borusyak & Xavier Jaravel & Jann Spiess, 2024. "Revisiting Event-Study Designs: Robust and Efficient Estimation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 91(6), pages 3253-3285.
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