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。该方法在两个维度上对时间序列回归树进行了泛化：首先，它能够处理存在测量误差、非平稳趋势、季节性和/或缺失观测等不规则性的预测变量；其次，它为利用领域特定理论来指导时间序列回归树提供了一种透明的方式。在实证中，这些因子增强树的集成系统为宏观金融问题提供了可靠方法。本文重点以美国股票波动性与商业周期之间的领先滞后效应为例加以说明。