Out-of-Sample Tests for Conditional Quantile Coverage - An Application to Growth-at-Risk

42 Pages Posted: 31 Aug 2020 Last revised: 12 Jul 2023

See all articles by Valentina Corradi

Valentina Corradi

University of Surrey - School of Economics

Jack Fosten

King’s College London - King's Business School

Daniel Gutknecht

Goethe University Frankfurt

Date Written: June 30, 2023

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.

Keywords: Interval Prediction, Quantile Regression, Multiple Hypothesis Testing, Weak Moment Inequalities, Wild Bootstrap, Growth-at-Risk.

JEL Classification: C01, C12, C22, C53

Suggested Citation

Corradi, Valentina and Fosten, Jack and Gutknecht, Daniel, Out-of-Sample Tests for Conditional Quantile Coverage - An Application to Growth-at-Risk (June 30, 2023). Journal of Econometrics, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3670575 or http://dx.doi.org/10.2139/ssrn.3670575

Valentina Corradi

University of Surrey - School of Economics ( email )

Guildford
Guildford, Surrey GU2 5XH
United Kingdom

Jack Fosten

King’s College London - King's Business School ( email )

150 Stamford Street
London, SE1 9NH
United Kingdom

Daniel Gutknecht (Contact Author)

Goethe University Frankfurt ( email )

Frankfurt am Main, 60629
Germany

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