  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.
