Two-way Clustering Robust Variance Estimator in Quantile Regression Models (opens in new tab)

arXiv:2602.16376v2 Announce Type: replace-cross 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 cons...