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arXiv · 2602.16376

Two-way Clustering Robust Variance Estimator in Quantile Regression Models

Ulrich Hounyo·Jiahao Lin
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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.

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Ulrich Hounyo, Jiahao Lin. 2026-02-18. Two-way Clustering Robust Variance Estimator in Quantile Regression Models. https://arxiv.org/abs/2602.16376

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