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- W4226175090 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." @default.
- W4226175090 created "2022-05-05" @default.
- W4226175090 creator A5012790341 @default.
- W4226175090 date "2021-11-27" @default.
- W4226175090 modified "2023-09-26" @default.
- W4226175090 title "Factor-augmented tree ensembles" @default.
- W4226175090 doi "https://doi.org/10.48550/arxiv.2111.14000" @default.
- W4226175090 hasPublicationYear "2021" @default.
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