Abstract:We study inference for linear quantile regression with two-way clustered data. Using a separately exchangeable array framework and a projection decomposition of the quantile score, we characterize regime-dependent convergence rates and establish a self-normalized Gaussian approximation. We propose a two-way cluster-robust sandwich variance estimator with a kernel-based density ``bread'' and a projection-matched ``meat'', and prove consistency and validity of inference in Gaussian regimes. We also show an impossibility result for uniform inference in a non-Gaussian interaction regime.
| Subjects: | Econometrics (econ.EM); Applications (stat.AP) |
| Cite as: | arXiv:2602.16376 [econ.EM] |
| (or arXiv:2602.16376v2 [econ.EM] for this version) | |
| https://doi.org/10.48550/arXiv.2602.16376 arXiv-issued DOI via DataCite |
From: Ulrich Hounyo [view email]
[v1]
Wed, 18 Feb 2026 11:35:18 UTC (852 KB)
[v2]
Sat, 23 May 2026 00:30:33 UTC (867 KB)
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