Nonparametric estimation and inference on conditional quantile processes

Title
Nonparametric estimation and inference on conditional quantile processes
Author
윤정모
Keywords
Nonparametric quantile regression; Uniform Bahadur representation; Uniform inference; Treatment effect
Issue Date
2015-03
Publisher
ELSEVIER SCIENCE SA
Citation
JOURNAL OF ECONOMETRICS, v. 185, NO 1, Page. 1-19
Abstract
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.
URI
http://www.sciencedirect.com/science/article/pii/S0304407614002462http://hdl.handle.net/20.500.11754/22977
ISSN
0304-4076; 1872-6895
DOI
10.1016/j.jeconom.2014.10.008
Appears in Collections:
COLLEGE OF ECONOMICS AND FINANCE[S](경제금융대학) > ECONOMICS & FINANCE(경제금융학부) > Articles
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