James G. MacKinnon, Morten Ørregaard Nielsen, Matthew D. Webb
arXiv 11 Jan 2023 · Econometrics · publishedJournal of Applied Econometrics (2023) · 42 citations (OpenAlex)
arXiv:2301.04527 · PDF · DOI · OpenAlex · Extracted main text
We provide computationally attractive methods to obtain jackknife-based cluster-robust variance matrix estimators (CRVEs) for linear regression models estimated by least squares. We also propose several new variants of the wild cluster bootstrap, which involve these CRVEs, jackknife-based bootstrap data-generating processes, or both. Extensive simulation experiments suggest that the new methods can provide much more reliable inferences than existing ones in cases where the latter are not trustworthy, such as when the number of clusters is small and/or cluster sizes vary substantially. Three empirical examples illustrate the new methods.
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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 | Djogbenou AA, Mac\-Kinnon JG, Nielsen M (2019) Asymptotic theory and wild bootstrap inference with clustered errors | 1.000 | 7 | 4 | 100% |
| 2 | Mac\-Kinnon JG, Nielsen M, Webb MD (2023) Cluster-robust inference: A guide to empirical practice | 1.000 | 7 | 4 | 100% |
| 3 | Bell RM, McCaffrey DF (2002) Bias reduction in standard errors for linear regression with multi-stage samples | 1.000 | 5 | 4 | 100% |
| 4 | Mac\-Kinnon JG (2022) Fast cluster bootstrap methods for linear regression models | 1.000 | 5 | 3 | 100% |
| 5 | Roodman D, Mac\-Kinnon JG, Nielsen M, Webb MD (2019) Fast and wild: Bootstrap inference in Stata using boottest | 1.000 | 5 | 3 | 100% |
| 6 | Mac\-Kinnon JG, Nielsen M, Webb MD (2022) Leverage, influence, and the jackknife in clustered regression models: Reliable inference using summclust | 0.928 | 4 | 3 | 100% |
| 7 | Hansen BE (2022) Jackknife standard errors for clustered regression | 0.843 | 3 | 3 | 100% |
| 8 | Mac\-Kinnon JG, Webb MD (2018) The wild bootstrap for few (treated) clusters | 0.737 | 3 | 2 | 100% |
| 9 | Mac\-Kinnon JG, Webb MD (2017) Wild bootstrap inference for wildly different cluster sizes | 0.737 | 3 | 2 | 100% |
| 10 | Mac\-Kinnon JG, White H (1985) Some heteroskedasticity consistent covariance matrix estimators with improved finite sample properties | 0.737 | 3 | 2 | 100% |
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