Two-way Clustering Robust Variance Estimator in Quantile Regression Models

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.