NONPARAMETRIC INFERENCE FOR CONDITIONAL QUANTILES OF TIME SERIES

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...

Full description

Saved in:
Bibliographic Details
Published in:Econometric theory 2013-08, Vol.29 (4), p.673-698
Main Author: Xu, Ke-Li
Format: Article
Language:English
Subjects:
Confidence interval
Cumulative distribution functions
Dependent variables
Distribution economics
Econometrics
Economic models
Economic theory
Estimators
Financial risk
Hypothesis
Hypothesis testing
Inference
Model testing
Point estimators
Rates of return
Simulation
Simulations
Statistical estimation
Stock returns
Studies
Theoretical econometrics
Time series
Value at risk
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0266-4666
1469-4360
DOI:10.1017/S0266466612000667