| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP14469 |
| DP14469 A Similarity-based Approach for Macroeconomic Forecasting | |
| Massimiliano Marcellino; George Kapetanios; Yiannis Dendramis | |
| 发表日期 | 2020-03-04 |
| 出版年 | 2020 |
| 语种 | 英语 |
| 摘要 | In the aftermath of the recent financial crisis there has been considerable focus on methods for predicting macroeconomic variables when their behavior is subject to abrupt changes, associated for example with crisis periods. In this paper we propose similarity based approaches as a way to handle parameter instability, and apply them to macroeconomic forecasting. The rationale is that clusters of past data that match the current economic conditions can be more informative for forecasting than the entire past behavior of the variable of interest. We apply our methods to predict both simulated data in a set of Monte Carlo experiments, and a broad set of key US macroeconomic indicators. The forecast evaluation exercises indicate that similarity-based approaches perform well, in general, in comparison with other common time-varying forecasting methods, and particularly well during crisis episodes. |
| 主题 | Monetary Economics and Fluctuations |
| 关键词 | Macroeconomic forecasting Forecast comparison Empirical similarity Parameter time variation Kernel estimation |
| URL | https://cepr.org/publications/dp14469 |
| 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/543368 |
| 推荐引用方式 GB/T 7714 | Massimiliano Marcellino,George Kapetanios,Yiannis Dendramis. DP14469 A Similarity-based Approach for Macroeconomic Forecasting. 2020. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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