G2TT
来源类型Discussion paper
规范类型论文
来源IDDP17461
DP17461 Tail Forecasting with Multivariate Bayesian Additive Regression Trees
Todd Clark; Florian Huber; Massimiliano Marcellino; Michael Pfarrhofer
发表日期2022-07-12
出版年2022
语种英语
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 US 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.
主题Monetary Economics and Fluctuations
关键词Nonparametric var Regression trees Macroeconomic forecasting Scenario analysis
URLhttps://cepr.org/publications/dp17461
来源智库Centre for Economic Policy Research (United Kingdom)
资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/546552
推荐引用方式
GB/T 7714
Todd Clark,Florian Huber,Massimiliano Marcellino,et al. DP17461 Tail Forecasting with Multivariate Bayesian Additive Regression Trees. 2022.
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