Two-way Clustering Robust Variance Estimator in Quantile Regression Models
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866911709793353728 |
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| author | Hounyo, Ulrich Lin, Jiahao |
| author_facet | Hounyo, Ulrich Lin, Jiahao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_16376 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Two-way Clustering Robust Variance Estimator in Quantile Regression Models Hounyo, Ulrich Lin, Jiahao Econometrics Applications 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. |
| title | Two-way Clustering Robust Variance Estimator in Quantile Regression Models |
| topic | Econometrics Applications |
| url | https://arxiv.org/abs/2602.16376 |