Nonparametric estimation and inference on conditional quantile processes

Zhongjun Qu and Jungmo Yoon

Journal of Econometrics, 2015, vol. 185, issue 1, 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.

Keywords: Nonparametric quantile regression; Uniform Bahadur representation; Uniform inference; Treatment effect (search for similar items in EconPapers)
JEL-codes: C14 C21 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (35)

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Related works:
Working Paper: Nonparametric Estimation and Inference on Conditional Quantile Processes (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:185:y:2015:i:1:p:1-19

DOI: 10.1016/j.jeconom.2014.10.008

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