TAIL FORECASTING WITH MULTIVARIATE BAYESIAN ADDITIVE REGRESSION TREES

• 成果类型: Article
• 署名作者: Clark, Todd E.; Huber, Florian; Koop, Gary; Marcellino, Massimiliano; Pfarrhofer, Michael
• 署名单位: Federal Reserve System - USA; Federal Reserve Bank - Cleveland; Salzburg University; University of Strathclyde; Bocconi University; University of Vienna
• 刊物名称: INTERNATIONAL ECONOMIC REVIEW
• ISSN/ISSBN: 0020-6598
• DOI: 10.1111/iere.12619
• 发表日期: 2023
• 页码: 979-1022
• 关键词: vector autoregressions; stochastic volatility; hierarchical priors; inference; GROWTH; shrinkage; variables; models

摘要

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.