This paper studies the extent to which macro-finance term structure models are susceptible to predictive uncertainty. We propose a general form of arbitrage-free models and quantify the relative importance of unpredictable priced risk variance, as well as macro-finance model uncertainty and learning uncertainty in predictability. Predictive performance and relative contributions of uncertainty sources are dynamically measured based on Bayesian methods, revealing dominating priced risk variance and other important uncertainty sources at different points in time. Macro-finance model uncertainty is high for near-term forward spread forecasts and contributes up to 87% of predictive uncertainty prior to recessions, implying strong dispersion in the information content of macro variables when forming near-term monetary policy expectations.

中文翻译：

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分解宏观金融期限结构模型中的不确定性

本文研究了宏观金融期限结构模型对预测不确定性的影响程度。我们提出了一种无套利模型的一般形式，并量化了不可预测的价格风险方差的相对重要性，以及宏观金融模型的不确定性和可预测性的学习不确定性。基于贝叶斯方法动态测量预测性能和不确定性源的相对贡献，揭示不同时间点的主导价格风险方差和其他重要的不确定性源。宏观金融模型对于近期远期利差预测的不确定性很高，并且在经济衰退之前贡献了高达 87% 的预测不确定性，这意味着在形成近期货币政策预期时宏观变量的信息内容存在很强的分散性。