A consistent specification test for dynamic quantile models
Peter Horvath, Jia Li, Zhipeng Liao, Andrew J. Patton
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.
Year of publication: |
2022
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Authors: | Horvath, Peter ; Li, Jia ; Liao, Zhipeng ; Patton, Andrew J. |
Published in: |
Quantitative economics : QE ; journal of the Econometric Society. - Oxford [u.a.] : Wiley, ISSN 1759-7331, ZDB-ID 2569569-1. - Vol. 13.2022, 1, p. 125-151
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Subject: | Bootstrap | series regression | strong approximation | VaR | Schätztheorie | Estimation theory | Bootstrap-Verfahren | Bootstrap approach | Statistischer Test | Statistical test | Regressionsanalyse | Regression analysis | Risikomaß | Risk measure | VAR-Modell | VAR model | Zeitreihenanalyse | Time series analysis |
Saved in:
Type of publication: | Article |
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Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
Language: | English |
Other identifiers: | 10.3982/QE1727 [DOI] hdl:10419/296271 [Handle] |
Classification: | C14 - Semiparametric and Nonparametric Methods ; C22 - Time-Series Models ; C52 - Model Evaluation and Testing |
Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://ebvufind01.dmz1.zbw.eu/10012807744
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