2020/11/05 by Grigory Franguridi, Franguridi, Grigory, Bulat Gafarov +3
Economics, Econometrics and Finance · Mathematics · #Econometrics (econ.EM) #Economics of Agriculture and Food Markets #FOS: Economics and business #FOS: Mathematics #Monetary Policy and Economic Impact #Probability (math.PR) #Statistical Methods and Inference #Statistics Theory (math.ST)
paper · pdf · doi:10.48550/arxiv.2011.03073
openalex publication_date 2020/11/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.