Bias correction for quantile regression estimators

Grigory Franguridi, Bulat Gafarov and Kaspar Wüthrich

Papers from arXiv.org

Abstract: We study the bias of classical quantile regression and instrumental variable quantile regression estimators. While being asymptotically first-order unbiased, these estimators can have non-negligible second-order biases. We derive a higher-order stochastic expansion of these estimators using empirical process theory. Based on this expansion, we derive an explicit formula for the second-order bias and propose a feasible bias correction procedure that uses finite-difference estimators of the bias components. The proposed bias correction method performs well in simulations. We provide an empirical illustration using Engel's classical data on household food expenditure.

Date: 2020-11, Revised 2025-02
New Economics Papers: this item is included in nep-ecm and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2011.03073

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