Out-of-sample tests for conditional quantile coverage an application to Growth-at-Risk
Corradi, V., Fosten, J. & Gutknecht, D. (2023). Out-of-sample tests for conditional quantile coverage an application to Growth-at-Risk. Journal of Econometrics, 236(2), article number 105490. doi: 10.1016/j.jeconom.2023.105490
Abstract
This paper proposes tests for out-of-sample comparisons of interval forecasts based on parametric conditional quantile models. The tests rank the distance between actual and nominal conditional coverage with respect to the set of conditioning variables from all models, for a given loss function. We propose a pairwise test to compare two models for a single predictive interval. The set-up is then extended to a comparison across multiple models and/or intervals. The limiting distribution varies depending on whether models are strictly non-nested or overlapping. In the latter case, degeneracy may occur. We establish the asymptotic validity of wild bootstrap based critical values across all cases. An empirical application to Growth-at-Risk (GaR) uncovers situations in which a richer set of financial indicators are found to outperform a commonly-used benchmark model when predicting downside risk to economic activity.
Publication Type: | Article |
---|---|
Additional Information: | This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Publisher Keywords: | Interval prediction, Quantile regression, Multiple hypothesis testing, Weak moment inequalities, Wild bootstrap, Growth-at-Risk |
Subjects: | H Social Sciences > HB Economic Theory |
Departments: | Bayes Business School Bayes Business School > Finance |
SWORD Depositor: |
Available under License Creative Commons Attribution.
Download (553kB) | Preview
Export
Downloads
Downloads per month over past year