James G. MacKinnon, Morten Ørregaard Nielsen, Matthew D. Webb
arXiv 6 May 2022 · Econometrics · publishedJournal of Econometrics (2022) · 314 citations (OpenAlex)
arXiv:2205.03285 · PDF · DOI · OpenAlex · Extracted main text
Methods for cluster-robust inference are routinely used in economics and many other disciplines. However, it is only recently that theoretical foundations for the use of these methods in many empirically relevant situations have been developed. In this paper, we use these theoretical results to provide a guide to empirical practice. We do not attempt to present a comprehensive survey of the (very large) literature. Instead, we bridge theory and practice by providing a thorough guide on what to do and why, based on recently available econometric theory and simulation evidence. To practice what we preach, we include an empirical analysis of the effects of the minimum wage on labor supply of teenagers using individual data.
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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 | Mac\-Kinnon, J.G., Nielsen, M.., Webb, M.D (2022) Fast jackknife and bootstrap methods for cluster-robust inference self | 1.000 | 8 | 4 | 100% |
| 2 | Mac\-Kinnon, J.G., Nielsen, M.., Webb, M.D (2022) Leverage, influence, and the jackknife in clustered regression models: Reliable inference using summclust self | 1.000 | 7 | 4 | 100% |
| 3 | Pustejovsky, J.E., Tipton, E (2018) Small sample methods for cluster-robust variance estimation and hypothesis testing in fixed effects models | 1.000 | 6 | 4 | 100% |
| 4 | Mac\-Kinnon, J.G., Webb, M.D (2017) 1Wild bootstrap inference for wildly different cluster sizes self | 1.000 | 6 | 3 | 100% |
| 5 | Bell, R.M., McCaffrey, D.F (2002) Bias reduction in standard errors for linear regression with multi-stage samples | 1.000 | 5 | 3 | 100% |
| 6 | Ibragimov, R., Müller, U.K (2016) Inference with few heterogeneous clusters | 1.000 | 5 | 3 | 100% |
| 7 | Mac\-Kinnon, J.G., Webb, M.D (2018) The wild bootstrap for few (treated) clusters self | 0.928 | 4 | 3 | 100% |
| 8 | Djogbenou, A.A., Mac\-Kinnon, J.G., Nielsen, M (2019) Asymptotic theory and wild bootstrap inference with clustered errors self | 0.874 | 11 | 2 | 100% |
| 9 | Mac\-Kinnon, J.G (2022) Fast cluster bootstrap methods for linear regression models | 0.874 | 8 | 2 | 100% |
| 10 | Bester, C.A., Conley, T.G., Hansen, C.B (2011) Inference with dependent data using cluster covariance estimators | 0.874 | 6 | 2 | 100% |
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