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LOCAL PARTITIONED QUANTILE REGRESSION

Published online by Cambridge University Press:  19 September 2016

Zhengyu Zhang
Zhengyu Zhang*
Affiliation:
Shanghai University of Finance and Economics
*
*Address correspondence to Zhengyu Zhang, School of Economics, Shanghai University of Finance and Economics, 777 Guoding Road, 200433 Shanghai, China; e-mail: zy.zhang@mail.shufe.edu.cn.
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Abstract

In this paper, we consider the nonparametric estimation of a broad class of quantile regression models, in which the partially linear, additive, and varying coefficient models are nested. We propose for the model a two-stage kernel-weighted least squares estimator by generalizing the idea of local partitioned mean regression (Christopeit and Hoderlein, 2006, Econometrica 74, 787–817) to a quantile regression framework. The proposed estimator is shown to have desirable asymptotic properties under standard regularity conditions. The new estimator has three advantages relative to existing methods. First, it is structurally simple and widely applicable to the general model as well as its submodels. Second, both the functional coefficients and their derivatives up to any given order can be estimated. Third, the procedure readily extends to censored data, including fixed or random censoring. A Monte Carlo experiment indicates that the proposed estimator performs well in finite samples. An empirical application is also provided.

Type
ARTICLES
Information
Econometric Theory , Volume 33 , Issue 5 , October 2017 , pp. 1081 - 1120
Copyright
Copyright © Cambridge University Press 2016 

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Footnotes

The author is grateful to the editor Peter C. B. Phillips, a co-editor and two anonymous referees for their constructive comments. Zhengyu Zhang is also affiliated with the Key Laboratory of Mathematical Economics (SUFE), Ministry of Education. The research is supported by the National Science Foundation of China (Grant No. 71501116). Zhengyu Zhang thanks Miffy Lee for her assistance in the research for this article.

References

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