Bias correction for quantile regression estimators

• Tác giả: Grigory Franguridi, Bulat Gafarov, Kaspar Wuthrich
• Ngôn ngữ: eng
• Ký hiệu phân loại: 526.35 Mathematical geography
• Thông tin xuất bản: 2020
• Bộ sưu tập: Metadata
• ID: 165539

Tóm tắt

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.