| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP15917 |
| DP15917 Conditional Rotation Between Forecasting Models | |
| Yinchu Zhu; Henry Allan Timmermann | |
| 发表日期 | 2021-03-14 |
| 出版年 | 2021 |
| 语种 | 英语 |
| 摘要 | We establish conditions under which forecasting performance can be improved by rotating between a set of underlying forecasts whose predictive accuracy is tracked using a set of time-varying monitoring instruments. We characterize the properties that the monitoring instruments must possess to be useful for identifying, at each point in time, the best forecast and show that these reflect both the accuracy of the predictors used by the underlying forecasting models and the strength of the monitoring instruments. Finite-sample bounds on forecasting performance that account for estimation error are used to compute the expected loss of the competing forecasts as well as for the dynamic rotation strategy. Finally, using Monte Carlo simulations and empirical applications to forecasting inflation and stock returns, we demonstrate the potential gains from using conditioning information to rotate between forecasts |
| 主题 | Financial Economics |
| 关键词 | Forecasting performance Real time monitoring Finite sample bounds |
| URL | https://cepr.org/publications/dp15917 |
| 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/544909 |
| 推荐引用方式 GB/T 7714 | Yinchu Zhu,Henry Allan Timmermann. DP15917 Conditional Rotation Between Forecasting Models. 2021. |
| 条目包含的文件 | 条目无相关文件。 | |||||
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
