Robust Tests of Forecast Accuracy for Factor‐Augmented Regressions With an Application to the Novel EA‐MD‐QD Dataset
提出了四种新的检验方法，用于评估因子增强回归中预测准确性和包含关系，这些方法对因子数量过度设定、预测变量持久性差异和结构断点位置具有稳健性，并应用于欧元区及其主要成员国的数据集。
ABSTRACT We present four novel tests of equal predictive accuracy and encompassing á Pitarakis (2023, 2025) for factor‐augmented regressions. Factors are estimated using cross‐section averages (CAs) of grouped series and our theoretical findings are empirically relevant: asymptotic normality, robustness to an overspecification of the number of factors, tractability of different degrees of predictor persistence, and invariance to the location of structural breaks in the loadings. Simulations reveal good local power properties of our tests. We apply them to the novel EA‐MD‐QD dataset by Barigozzi et al. (2024b)—which covers the Euro Area as a whole and its primary member countries—and show that factors offer predictive power.