arXiv 2 Apr 2026 · Econometrics
arXiv:2604.02000 · PDF · DOI · OpenAlex · Extracted main text
It is common when using cross-section or panel data to assign each observation to a cluster and allow for arbitrary patterns of heteroskedasticity and correlation within clusters. For regression models, there are many ways to make cluster-robust inferences. A number of different variance matrix estimators can be used. Hypothesis tests and confidence intervals can then be based on several alternative analytic or bootstrap distributions. Some methods typically perform much better than others, but no method yields reliable inferences in every case. Thus it can be hard to know which $P$ values and confidence intervals to trust. Nevertheless, by using a number of procedures to assess the reliability of various inferential methods for a specific model and dataset, we can often obtain results in which we may be reasonably confident.
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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 | James G. Mac\-Kinnon and Morten Ø. Nielsen and Matthew D. Webb (2023) Fast jackknife and bootstrap methods for cluster-robust inference | 1.000 | 12 | 4 | 100% |
| 2 | Bruce E. Hansen (2025) Jackknife standard errors for clustered regression | 1.000 | 9 | 4 | 100% |
| 3 | Bruce E. Hansen (2025) Standard errors for difference-in-difference regression | 1.000 | 8 | 4 | 100% |
| 4 | Antoine A. Djogbenou and James G. Mac\-Kinnon and Morten Ø. Nielsen (2019) Asymptotic theory and wild bootstrap inference with clustered errors | 1.000 | 7 | 4 | 100% |
| 5 | Bester, C. Alan and Conley, Timothy G. and Hansen, Christian B (2011) Inference with dependent data using cluster covariance estimators | 0.928 | 4 | 3 | 100% |
| 6 | Cameron, A. Colin and Gelbach, Jonah B. and Miller, Douglas L (2008) Bootstrap-based improvements for inference with clustered errors | 0.811 | 4 | 2 | 100% |
| 7 | James G. Mac\-Kinnon and Morten Ø. Nielsen and Matthew D. Webb (2023) Leverage, influence, and the jackknife in clustered regression models: Reliable inference using summclust | 0.811 | 4 | 2 | 100% |
| 8 | James G. Mac\-Kinnon and Morten Ø. Nielsen and Matthew D. Webb (2025) Cluster-robust jackknife and bootstrap inference for logistic regression models | 0.811 | 4 | 2 | 100% |
| 9 | James G. Mac\-Kinnon and Morten Ø. Nielsen and Matthew D. Webb (2023) Testing for the appropriate level of clustering in linear regression models | 0.737 | 3 | 2 | 100% |
| 10 | James G. Mac\-Kinnon and Matthew D. Webb (2018) The wild bootstrap for few (treated) clusters | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 54 scored citations.