This manuscript proposes to extend the information set of time-series regression trees with latent stationary factors extracted via state-space methods. In doing so, this approach generalises time-series regression trees on two dimensions. First, it allows to handle predictors that exhibit measurement error, non-stationary trends, seasonality and/or irregularities such as missing observations. Second, it gives a transparent way for using domain-specific theory to inform time-series regression trees. Empirically, ensembles of these factor-augmented trees provide a reliable approach for macro-finance problems. This article highlights it focussing on the lead-lag effect between equity volatility and the business cycle in the United States.

翻译：本文提出通过状态空间方法提取潜在平稳因子，以扩展时间序列回归树的信息集。该方法从两个维度对时间序列回归树进行了泛化：第一，能够处理具有测量误差、非平稳趋势、季节性以及缺失观测等不规则性的预测变量；第二，为利用领域特定理论指导时间序列回归树提供了透明化的途径。实证表明，这些因子增强树的集成方法为宏观金融问题提供了可靠的研究框架。本文聚焦美国股市波动性与商业周期之间的领先滞后效应，对该方法进行了重点阐释。