Harold D. Chiang, Yuya Sasaki, Yulong Wang
arXiv 20 Aug 2023 · Econometrics · 2 citations (OpenAlex)
arXiv:2308.10138 · PDF · DOI · OpenAlex · Extracted main text
Conventional cluster-robust inference can be invalid when data contain clusters of unignorably large size. We formalize this issue by deriving a necessary and sufficient condition for its validity, and show that this condition is frequently violated in practice: specifications from 77% of empirical research articles in American Economic Review and Econometrica during 2020-2021 appear not to meet it. To address this limitation, we propose a genuinely robust inference procedure based on a new cluster score bootstrap. We establish its validity and size control across broad classes of data-generating processes where conventional methods break down. Simulation studies corroborate our theoretical findings, and empirical applications illustrate that employing the proposed method can substantially alter conventional statistical conclusions.
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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 | MacKinnon, J. G., M. . Nielsen, and M. D. Webb (2022) Fast and reliable jackknife and bootstrap methods for cluster-robust inference | 0.843 | 4 | 3 | 75% |
| 2 | Sasaki, Y. and Y. Wang (2023) Diagnostic Testing of Finite Moment Conditions for the Consistency and Root-$N$ Asymptotic Normality of the GMM and M Estimators self | 0.811 | 4 | 2 | 100% |
| 3 | Geluk, J. L. and L. de Haan (2000) Stable probability distributions and their domains of attraction: a direct approach | 0.737 | 3 | 3 | 67% |
| 4 | Bickel, P. J. and A. Sakov (2008) On the choice of m in the m out of n bootstrap and confidence bounds for extrema | 0.737 | 3 | 3 | 67% |
| 5 | Knight, K (1989) On the bootstrap of the sample mean in the infinite variance case | 0.737 | 3 | 3 | 67% |
| 6 | Bugni, F., I. Canay, A. Shaikh, and M. Tabord-Meehan (2024) Inference for cluster randomized experiments with non-ignorable cluster sizes | 0.737 | 3 | 2 | 100% |
| 7 | Athreya, K (1987) Bootstrap of the mean in the infinite variance case | 0.644 | 2 | 2 | 100% |
| 8 | Bai, Y., J. Liu, A. M. Shaikh, and M. Tabord-Meehan (2022) Inference in Cluster Randomized Trials with Matched Pairs | 0.644 | 2 | 2 | 100% |
| 9 | Cavaliere, G., T. Mikosch, A. Rahbek, and F. Vilandt (2024) Tail behavior of ACD models and consequences for likelihood-based estimation | 0.644 | 2 | 2 | 100% |
| 10 | Logan, B. F., C. Mallows, S. Rice, and L. A. Shepp (1973) Limit distributions of self-normalized sums | 0.585 | 4 | 3 | 25% |
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