Nonparametric estimation and inference on conditional quantile processesopen access
- Authors
- Qu, Zhongjun; Yoon, Jung mo
- Issue Date
- Mar-2015
- Publisher
- ELSEVIER SCIENCE SA
- Keywords
- Nonparametric quantile regression; Treatment effect; Uniform Bahadur representation; Uniform inference
- Citation
- JOURNAL OF ECONOMETRICS, v.185, no.1, pp.1 - 19
- Indexed
- SCIE
SSCI
SCOPUS
- Journal Title
- JOURNAL OF ECONOMETRICS
- Volume
- 185
- Number
- 1
- Start Page
- 1
- End Page
- 19
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/157652
- DOI
- 10.1016/j.jeconom.2014.10.008
- ISSN
- 0304-4076
- 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.
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