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A Simple Nonparametric Approach For Estimation And Inference Of Conditional Quantile Functions

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  • Fang, Zheng
  • Li, Qi
  • Yan, Karen X.

Abstract

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.

Suggested Citation

  • Fang, Zheng & Li, Qi & Yan, Karen X., 2023. "A Simple Nonparametric Approach For Estimation And Inference Of Conditional Quantile Functions," Econometric Theory, Cambridge University Press, vol. 39(2), pages 290-320, April.
    • Handle: RePEc:cup:etheor:v:39:y:2023:i:2:p:290-320_3
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    Cited by:

    1. Zhang, Feipeng & Xu, Yixiong & Fan, Caiyun, 2023. "Nonparametric inference of expectile-based value-at-risk for financial time series with application to risk assessment," International Review of Financial Analysis, Elsevier, vol. 90(C).

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