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A SIMPLE NONPARAMETRIC APPROACH FOR ESTIMATION AND INFERENCE OF CONDITIONAL QUANTILE FUNCTIONS

Published online by Cambridge University Press:  13 December 2021

Zheng Fang ,
Qi Li  and
Karen X. Yan
Zheng Fang
Affiliation:
Texas A&M University
Qi Li*
Affiliation:
Texas A&M University
Karen X. Yan
Affiliation:
Georgia Institute of Technology
*
Address correspondence to Qi Li, Department of Economics, Texas A&M University, College Station, TX, USA; e-mail qi-li@tamu.edu.
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Abstract

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In this paper, we present a new nonparametric method for estimating a conditional quantile function and develop its weak convergence theory. The proposed estimator is computationally easy to implement and automatically ensures quantile monotonicity by construction. For inference, we propose to use a residual bootstrap method. Our Monte Carlo simulations show that this new estimator compares well with the check-function-based estimator in terms of estimation mean squared error. The bootstrap confidence bands yield adequate coverage probabilities. An empirical example uses a dataset of Canadian high school graduate earnings, illustrating the usefulness of the proposed method in applications.

Type
ARTICLES
Information
Econometric Theory , Volume 39 , Issue 2 , April 2023 , pp. 290 - 320
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2021. Published by Cambridge University Press

Footnotes

We would like to thank the Editor, Peter Phillips, a Co-Editor, and three anonymous referees for helpful comments and suggestions, which have substantially improved the paper.

References

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