Publication Type
Journal Article
Version
submittedVersion
Publication Date
6-2022
Abstract
Correct specification of a conditional quantile model implies that a particular conditional moment is equal to zero. We nonparametrically estimate the conditional moment function via series regression and test whether it is identically zero using uniform functional inference. Our approach is theoretically justified via a strong Gaussian approximation for statistics of growing dimensions in a general time series setting. We propose a novel bootstrap method in this nonstandard context and show that it significantly outperforms the benchmark asymptotic approximation in finite samples, especially for tail quantiles such as Value-at-Risk (VaR). We use the proposed new test to study the VaR and CoVaR (Adrian and Brunnermeier (2016)) of a collection of US financial institutions.
Keywords
bootstrap, VaR, series regression, strong approximation
Discipline
Econometrics
Research Areas
Econometrics
Publication
Quantitative Economics
Volume
13
Issue
1
First Page
125
Last Page
151
ISSN
1759-7323
Identifier
10.3982/QE1727
Publisher
Econometric Society
Citation
HORVATH, Peter; LI, Jia; LIAO, Zhipeng; and PATTON, Andrew J..
A consistent specification test for dynamic quantile models. (2022). Quantitative Economics. 13, (1), 125-151.
Available at: https://ink.library.smu.edu.sg/soe_research/2555
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.3982/QE1727