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Genuinely Robust Inference for Clustered Data

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  • Harold D. Chiang
  • Yuya Sasaki
  • Yulong Wang

Abstract

Conventional cluster-robust inference methods are inconsistent when clusters are unignorably large. We derive a necessary and sufficient condition for consistency, which is violated in 77% of empirical studies published in American Economic Review and Econometrica (2020-2021). To address this, we propose two methods: (i) score subsampling, which retains the original estimator, and (ii) size-adjusted reweighting, which is easy to implement in software like Stata and remains valid if the cluster size follows Zipf's law. Simulations confirm the reliability and uniform size control of these approaches, offering robust alternatives where conventional methods fail.

Suggested Citation

  • Harold D. Chiang & Yuya Sasaki & Yulong Wang, 2023. "Genuinely Robust Inference for Clustered Data," Papers 2308.10138, arXiv.org, revised Jan 2025.
  • Handle: RePEc:arx:papers:2308.10138
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    References listed on IDEAS

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    1. Davidson, James, 1994. "Stochastic Limit Theory: An Introduction for Econometricians," OUP Catalogue, Oxford University Press, number 9780198774037, Decembrie.
    2. Nicholas M. Kiefer & Timothy J. Vogelsang, 2002. "Heteroskedasticity-Autocorrelation Robust Standard Errors Using The Bartlett Kernel Without Truncation," Econometrica, Econometric Society, vol. 70(5), pages 2093-2095, September.
    3. A. Colin Cameron & Douglas L. Miller, 2015. "A Practitioner’s Guide to Cluster-Robust Inference," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 317-372.
    4. Yuya Sasaki & Yulong Wang, 2022. "Non-Robustness of the Cluster-Robust Inference: with a Proposal of a New Robust Method," Papers 2210.16991, arXiv.org, revised Jan 2025.
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    Cited by:

    1. Bastien Buchwalter & Francis X. Diebold & Kamil Yilmaz, 2025. "Clustered Network Connectedness: A New Measurement Framework with Application to Global Equity Markets," Papers 2502.15458, arXiv.org.

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