James G. MacKinnon, Morten Ørregaard Nielsen, Matthew D. Webb
arXiv 13 Jun 2024 · Econometrics
arXiv:2406.08880 · PDF · DOI · OpenAlex · Extracted main text
For linear regression models with cross-section or panel data, it is natural to assume that the disturbances are clustered in two dimensions. However, the finite-sample properties of two-way cluster-robust tests and confidence intervals are often poor. We discuss several ways to improve inference with two-way clustering. Two of these are existing methods for avoiding, or at least ameliorating, the problem of undefined standard errors when a cluster-robust variance matrix estimator (CRVE) is not positive definite. One is a new method that always avoids the problem. More importantly, we propose a family of new two-way CRVEs based on the cluster jackknife. Simulations for models with two-way fixed effects suggest that, in many cases, the cluster-jackknife CRVE combined with our new method yields surprisingly accurate inferences. We provide a simple software package, twowayjack for Stata, that implements our recommended variance estimator.
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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 (2021) Wild bootstrap and asymptotic inference with multiway clustering self | 1.000 | 9 | 6 | 100% |
| 2 | James G. Mac\-Kinnon and Morten Ø. Nielsen and Matthew D. Webb (2023) Fast jackknife and bootstrap methods for cluster-robust inference self | 1.000 | 9 | 4 | 100% |
| 3 | Bruce E. Hansen (2025) Jackknife standard errors for clustered regression | 1.000 | 8 | 4 | 100% |
| 4 | Cameron, A. Colin and Gelbach, Jonah B. and Miller, Douglas L (2011) Robust inference with multiway clustering | 1.000 | 5 | 4 | 100% |
| 5 | Laurent Davezies and Xavier D'Haultfœuille and Yannick Guyonvarch (2025) Analytic inference with two-way clustering | 0.928 | 4 | 3 | 100% |
| 6 | 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 self | 0.909 | 8 | 5 | 75% |
| 7 | Luther Yap (2025) Asymptotic theory for two-way clustering | 0.874 | 9 | 3 | 67% |
| 8 | Antoine A. Djogbenou and James G. Mac\-Kinnon and Morten Ø. Nielsen (2019) Asymptotic theory and wild bootstrap inference with clustered errors self | 0.843 | 4 | 4 | 75% |
| 9 | Bruce E. Hansen (2025) Standard errors for difference-in-difference regression | 0.843 | 3 | 3 | 100% |
| 10 | James G. Mac\-Kinnon and Morten Ø. Nielsen and Matthew D. Webb (2023) Cluster-robust inference: A guide to empirical practice self | 0.843 | 3 | 3 | 100% |
Showing the top 10 of 37 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Robust Inference for Dyadic Data with Dependent Ordered Nodes | 0.644 | 2 | 2 |
| 2 | Analytic inference with two-way clustering | 0.511 | 2 | 1 |