| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP17461 |
| 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 |
| URL | https://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. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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