Factor-augmented tree ensembles
This work addresses macro-finance problems by improving time-series regression trees to handle measurement errors and non-stationarity, though it appears incremental as it builds on existing tree and factor methods.
The authors tackled the problem of handling noisy and irregular time-series predictors in macro-finance by extending regression trees with latent stationary factors, resulting in ensembles that provide a reliable approach for such problems, as demonstrated in analyzing the lead-lag effect between equity volatility and the business cycle in the United States.
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.