TY  - JOUR
A1  - Corradi, Valentina
A1  - Fosten, Jack
A1  - Gutknecht, Daniel
T1  - Out-of-sample tests for conditional quantile coverage an application to Growth-at-Risk
T2  - Journal of Econometrics
N2  - 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.
KW  - Interval prediction
KW  - Quantile regression
KW  - Multiple hypothesis testing
KW  - Weak moment inequalities
KW  - Wild bootstrap
KW  - Growth-at-Risk
Y1  - 2023
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/76273
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-762730
SN  - 0304-4076
VL  - 236
IS  - 2, art. 105490
PB  - Elsevier
CY  - Amsterdam
ER  -