Bias correction for quantile regression estimators

IF 9.9 3区 经济学 Q1 ECONOMICS
Grigory Franguridi , Bulat Gafarov , Kaspar Wüthrich
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引用次数: 0

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.
分位数回归估计的偏差校正
我们研究了经典分位数回归和工具变量分位数回归估计的偏差。虽然渐近一阶无偏,但这些估计量可能具有不可忽略的二阶偏。利用经验过程理论导出了这些估计量的高阶随机展开式。在此基础上,我们导出了二阶偏置的显式公式,并提出了一种可行的偏置校正方法,该方法使用偏置分量的有限差分估计量。所提出的偏置校正方法在仿真中取得了良好的效果。我们使用恩格尔关于家庭食品支出的经典数据提供了一个实证说明。
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来源期刊
Journal of Econometrics
Journal of Econometrics 社会科学-数学跨学科应用
CiteScore
8.60
自引率
1.60%
发文量
220
审稿时长
3-8 weeks
期刊介绍: The Journal of Econometrics serves as an outlet for important, high quality, new research in both theoretical and applied econometrics. The scope of the Journal includes papers dealing with identification, estimation, testing, decision, and prediction issues encountered in economic research. Classical Bayesian statistics, and machine learning methods, are decidedly within the range of the Journal''s interests. The Annals of Econometrics is a supplement to the Journal of Econometrics.
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