Nonparametric estimation and inference on conditional quantile processes

作     者：Qu, Zhongjun Yoon, Jungmo 

作者机构：Boston Univ Dept Econ Boston MA 02215 USA Claremont Mckenna Coll Robert Day Sch Econ & Finance Claremont CA 91711 USA 

出 版 物：《JOURNAL OF ECONOMETRICS》 (经济计量学杂志)

年 卷 期：2015年第185卷第1期

页      面：1-19页

核心收录：

学科分类：02[经济学] 0201[经济学-理论经济学] 0701[理学-数学] 

主　　题：Nonparametric quantile regression Uniform Bahadur representation Uniform inference Treatment effect 

摘      要：This paper presents estimation methods and asymptotic theory for the analysis of a nonparametrically specified conditional quantile process. Two estimators based on local linear regressions are proposed. The first estimator applies simple inequality constraints while the second uses rearrangement to maintain quantile monotonicity. The bandwidth parameter is allowed to vary across quantiles to adapt to data sparsity. For inference, the paper first establishes a uniform Bahadur representation and then shows that the two estimators converge weakly to the same limiting Gaussian process. As an empirical illustration, the paper considers a dataset from Project STAR and delivers two new findings. (C) 2014 Elsevier B.V. All rights reserved.