TAIL FORECASTING WITH MULTIVARIATE BAYESIAN ADDITIVE REGRESSION TREES

Todd Clark, Florian Huber, Gary Koop, Massimiliano Marcellino and Michael Pfarrhofer

International Economic Review, 2023, vol. 64, issue 3, 979-1022

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.

Date: 2023
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Citations: View citations in EconPapers (13)

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https://doi.org/10.1111/iere.12619

Related works:
Working Paper: Tail Forecasting with Multivariate Bayesian Additive Regression Trees (2022)
Working Paper: Tail Forecasting with Multivariate Bayesian Additive Regression Trees (2022)
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