James G. MacKinnon, Morten Ørregaard Nielsen, Matthew D. Webb
arXiv 11 Jan 2023 · Econometrics · publishedJournal of Econometrics (2023) · 30 citations (OpenAlex)
arXiv:2301.04522 · PDF · DOI · OpenAlex · Extracted main text
The overwhelming majority of empirical research that uses cluster-robust inference assumes that the clustering structure is known, even though there are often several possible ways in which a dataset could be clustered. We propose two tests for the correct level of clustering in regression models. One test focuses on inference about a single coefficient, and the other on inference about two or more coefficients. We provide both asymptotic and wild bootstrap implementations. The proposed tests work for a null hypothesis of either no clustering or “fine” clustering against alternatives of “coarser” clustering. We also propose a sequential testing procedure to determine the appropriate level of clustering. Simulations suggest that the bootstrap tests perform very well under the null hypothesis and can have excellent power. An empirical example suggests that using the tests leads to sensible inferences.
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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 | Cai, Y (2022) A modified randomization test for the level of clustering | 1.000 | 6 | 3 | 100% |
| 2 | Ibragimov, R. and U. K. Müller (2016) Inference with few heterogeneous clusters | 1.000 | 5 | 3 | 100% |
| 3 | Djogbenou, A. A., J. G. Mac\-Kinnon, and M. . Nielsen (2019) Asymptotic theory and wild bootstrap inference with clustered errors | 0.965 | 10 | 6 | 90% |
| 4 | 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 | 3 | 100% |
| 5 | Mac\-Kinnon, J. G. and M. D. Webb (2017) Wild bootstrap inference for wildly different cluster sizes | 0.928 | 4 | 3 | 100% |
| 6 | 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% |
| 7 | Mac\-Kinnon, J. G. and M. D. Webb (2018) The wild bootstrap for few (treated) clusters | 0.843 | 3 | 3 | 100% |
| 8 | Cameron, A. C., J. B. Gelbach, and D. L. Miller (2008) Bootstrap-based improvements for inference with clustered errors | 0.737 | 3 | 2 | 100% |
| 9 | Bertrand, M., E. Duflo, and S. Mullainathan (2004) How much should we trust differences-in-differences estimates? | 0.644 | 2 | 2 | 100% |
| 10 | de Chaisemartin, C. and J. Ramirez-Cuellar (2022) At what level should one cluster standard errors in paired experiments, and in stratified experiments with small strata? | 0.644 | 2 | 2 | 100% |
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