Out-of-sample tests for conditional quantile coverage an application to Growth-at-Risk
提出了基于参数条件分位数模型的区间预测样本外比较检验方法，通过排序实际与名义条件覆盖率的距离来比较模型，并应用于增长风险分析，发现更丰富的金融指标优于常用基准模型。
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.