Tail forecasting with multivariate Bayesian additive regression trees

Clark, Todd E. and Huber, Florian and Koop, Gary and Marcellino, Massimiliano and Pfarrhofer, Michael (2023) Tail forecasting with multivariate Bayesian additive regression trees. International Economic Review, 64 (3). pp. 979-1022. ISSN 0020-6598 (https://doi.org/10.1111/iere.12619)

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Abstract

We develop multivariate time-series models using Bayesian additive regression trees that posit nonlinearities among macroeconomic variables, their lags, and possibly their lagged errors. The error variances can be stable, feature stochastic volatility, or follow a nonparametric specification. We evaluate density and tail forecast performance for a set of U.S. macroeconomic and financial indicators. Our results suggest that the proposed models improve forecast accuracy both overall and in the tails. Another finding is that when allowing for nonlinearities in the conditional mean, heteroskedasticity becomes less important. A scenario analysis reveals nonlinear relations between predictive distributions and financial conditions.

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