| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP15867 |
| DP15867 Can Machine Learning Catch the COVID-19 Recession? | |
| Massimiliano Marcellino; Dalibor Stevanovic; Philippe Goulet Coulombe | |
| 发表日期 | 2021-03-02 |
| 出版年 | 2021 |
| 语种 | 英语 |
| 摘要 | Based on evidence gathered from a newly built large macroeconomic data set for the UK, labeled UK-MD and comparable to similar datasets for the US and Canada, it seems the most promising avenue for forecasting during the pandemic is to allow for general forms of nonlinearity by using machine learning (ML) methods. But not all nonlinear ML methods are alike. For instance, some do not allow to extrapolate (like regular trees and forests) and some do (when complemented with linear dynamic components). This and other crucial aspects of ML-based forecasting in unprecedented times are studied in an extensive pseudo-out-of-sample exercise. |
| 主题 | Monetary Economics and Fluctuations |
| URL | https://cepr.org/publications/dp15867 |
| 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/544859 |
| 推荐引用方式 GB/T 7714 | Massimiliano Marcellino,Dalibor Stevanovic,Philippe Goulet Coulombe. DP15867 Can Machine Learning Catch the COVID-19 Recession?. 2021. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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