arXiv 1 May 2026 · Mathematics — Statistics Theory
arXiv:2605.00709 · PDF · DOI · OpenAlex · Extracted main text
This paper develops bootstrap procedures for inference in linear regression models with two-way clustered data. We characterize the estimator's asymptotic behavior in five mutually exclusive and exhaustive regimes: three Gaussian and two non-Gaussian. We establish four impossibility results: heterogeneous score components preclude uniform consistency; uniform consistency also fails in one non-Gaussian (infeasible) regime; the infeasible regime is not uniformly distinguishable from a feasible one; and uniform validity over all feasible regimes rules out uniform conservativeness over the infeasible regime. To address the feasible regimes, we propose a data-driven regime classifier and a projection-based wild bootstrap procedure. The procedure delivers uniformly valid inference across the four feasible regimes while allowing serial dependence along the second clustering dimension and spatial dependence along the first. This combination of regime adaptivity and flexible dependence is new to the two-way clustering literature. Monte Carlo simulations confirm the accuracy and flexibility of the proposed methods in settings with complex clustering structures.
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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 | Menzel, Konrad (2021) Bootstrap with cluster-dependence in two or more dimensions | 0.974 | 13 | 4 | 92% |
| 2 | Juodis, Artūras (2025) THIS SHOCK IS DIFFERENT: ESTIMATION AND INFERENCE IN MISSPECIFIED TWO-WAY FIXED EFFECTS PANEL REGRESSIONS | 0.874 | 7 | 2 | 100% |
| 3 | Chiang, Harold D. and Hansen, Bruce E. and Sasaki, Yuya (2024) Standard Errors for Two-Way Clustering with Serially Correlated Time Effects | 0.874 | 6 | 4 | 67% |
| 4 | MacKinnon, James G and Nielsen, Morten Ørregaard and Webb, Matthew D (2021) Wild bootstrap and asymptotic inference with multiway clustering | 0.874 | 5 | 2 | 100% |
| 5 | Conley, Timothy G (1999) GMM estimation with cross sectional dependence | 0.843 | 5 | 3 | 60% |
| 6 | Conley, Timothy G and Molinari, Francesca (2007) Spatial correlation robust inference with errors in location or distance | 0.644 | 2 | 2 | 100% |
| 7 | Davezies, Laurent and D'Haultfœuille, Xavier and Guyonvarch, Yannick (2025) Analytic inference with two-way clustering | 0.644 | 2 | 2 | 100% |
| 8 | Hounyo, Ulrich and Lin, Jiahao (2025) Wild bootstrap inference with multiway clustering and serially correlated time effects self | 0.644 | 2 | 2 | 100% |
| 9 | Chen, Mingli and Fernández-Val, Iván and Weidner, Martin (2021) Nonlinear factor models for network and panel data | 0.511 | 2 | 2 | 50% |
| 10 | Fernández-Val, Iván and Freeman, Hugo and Weidner, Martin (2021) Low-rank approximations of nonseparable panel models | 0.511 | 2 | 2 | 50% |
Showing the top 10 of 36 scored citations.