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Two-way Clustering Robust Variance Estimator in Quantile Regression Models

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  • Ulrich Hounyo
  • Jiahao Lin

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.

Suggested Citation

  • Ulrich Hounyo & Jiahao Lin, 2026. "Two-way Clustering Robust Variance Estimator in Quantile Regression Models," Papers 2602.16376, arXiv.org.
    • Handle: RePEc:arx:papers:2602.16376
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    File URL: http://arxiv.org/pdf/2602.16376
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