This study systematically broadens the relevance of possible model performance asymmetries across business cycles in the spirit of the recent state‐dependent forecast evaluation literature (e.g. Chauvet & Potter, 2013) to hundreds of macroeconomic indicators and deepens the forecast evaluation of the recent factor model literature on hundreds of target variables (e.g. Stock & Watson, 2012b) in a state‐dependent manner. Our results are consistent with both strands of the literature and generalize the former to over 200 macroeconomic indicators and differentiate the latter across three levels of temporal granularity: We document systematic model performance differences in both absolute and relative terms across business cycles (longitudinal) as well as across variable groups (cross‐sectional) and find these performance differences to be robust across several alternative specifications. The cross‐sectional prevalence and robustness of state dependency shown in this article encourages economic forecasters to complement model performance assessments with a state‐dependent evaluation of predictive ability.

中文翻译：

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状态依赖的预测能力评估

本研究本着最新的国家相关预测评估文献（例如Chauvet＆Potter，2013）的精神，系统地将跨业务周期的模型绩效不对称性的相关性扩大到数百种宏观经济指标，并加深了近期因素模型文献的预测评估。以状态依赖的方式处理数百个目标变量（例如Stock＆Watson，2012b）。我们的结果与这两方面的文献都是一致的，并且将前者概括为200多种宏观经济指标，并将后者在时间粒度的三个层次上进行区分：我们记录了整个业务周期（纵向）以及变量组（横截面）中的绝对和相对术语的系统模型性能差异，并发现这些性能差异在多个替代规范中均很可靠。本文显示的横断面患病率和国家依存度的稳健性鼓励经济预测者通过以下方法补充模型绩效评估：预测能力的状态依赖评估。