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NONPARAMETRIC INFERENCE FOR CONDITIONAL QUANTILES OF TIME SERIES

Published online by Cambridge University Press:  16 January 2013

Ke-Li Xu
Ke-Li Xu*
Affiliation:
Texas A&M University
*
*Address correspondence to Ke-Li Xu, 3063 Allen, 4228 TAMU, Department of Economics, Texas A&M University, College Station, TX 77843-4228, USA; e-mail: keli.xu@econmail.tamu.edu.
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Abstract

This paper considers model-free hypothesis testing and confidence interval construction for conditional quantiles of time series. A new method, which is based on inversion of the smoothed empirical likelihood of the conditional distribution function around the local polynomial estimator, is proposed. The associated inferential procedures do not require variance estimation, and the confidence intervals are automatically shaped by data. We also construct the bias-corrected empirical likelihood, which does not require undersmoothing. Limit theories are developed for mixing time series. Simulations show that the proposed methods work well in finite samples and outperform the normal confidence interval. An empirical application to inference of the conditional value-at-risk of stock returns is also provided.

Type
ARTICLES
Information
Econometric Theory , Volume 29 , Issue 4 , August 2013 , pp. 673 - 698
Copyright
Copyright © Cambridge University Press 2013 

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