重印本:条件量子覆盖的样本外测试:风险增长的应用

IF 9.9 3区 经济学 Q1 ECONOMICS
Valentina Corradi , Jack Fosten , Daniel Gutknecht
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引用次数: 0

摘要

本文提出了基于参数条件量子模型的区间预测样本外比较检验。在给定的损失函数下,这些检验对所有模型的条件变量集的实际条件覆盖率与名义条件覆盖率之间的距离进行排序。我们提出了一种成对测试,用于比较单个预测区间的两个模型。然后,我们将这一设置扩展到多个模型和/或区间的比较。极限分布因模型是严格非嵌套还是重叠而异。在后一种情况下,可能会出现退化。我们在所有情况下都确定了基于野生自举法的临界值的渐近有效性。通过对风险增长(GaR)的实证应用,我们发现在预测经济活动下行风险时,一组更丰富的金融指标优于常用的基准模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reprint of: Out-of-sample tests for conditional quantile coverage: An application to Growth-at-Risk
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.
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来源期刊
Journal of Econometrics
Journal of Econometrics 社会科学-数学跨学科应用
CiteScore
8.60
自引率
1.60%
发文量
220
审稿时长
3-8 weeks
期刊介绍: The Journal of Econometrics serves as an outlet for important, high quality, new research in both theoretical and applied econometrics. The scope of the Journal includes papers dealing with identification, estimation, testing, decision, and prediction issues encountered in economic research. Classical Bayesian statistics, and machine learning methods, are decidedly within the range of the Journal''s interests. The Annals of Econometrics is a supplement to the Journal of Econometrics.
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