Factor-augmented tree ensembles

• URL: http://arxiv.org/abs/2111.14000v6
• Date: Mon, 12 Jun 2023 19:37:37 GMT
• Title: Factor-augmented tree ensembles
• Authors: Filippo Pellegrino
• Abstract summary: This manuscript proposes to extend the information set of time-series regression trees with latent stationary factors extracted via state-space methods. It allows to handle predictors that exhibit measurement error, non-stationary trends, seasonality and/or irregularities such as missing observations. Empirically, ensembles of these factor-augmented trees provide a reliable approach for macro-finance problems.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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