Tail forecasting with multivariate Bayesian additive regression trees

“Tail Forecasting with Multivariate Bayesian Additive Regression Trees” [DOI], with Todd Clark, Florian Huber, Gary Koop and Massimiliano Marcellino, on multivariate nonparametric methods for capturing macroeconomic tail risks is now forthcoming in the International Economic Review.

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.