| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w28302 |
| 来源ID | Working Paper 28302 |
| Answering the Queen: Machine Learning and Financial Crises | |
| Jeremy Fouliard; Michael Howell; Hélène Rey | |
| 发表日期 | 2021-01-04 |
| 出版年 | 2021 |
| 语种 | 英语 |
| 摘要 | Financial crises cause economic, social and political havoc. Macroprudential policies are gaining traction but are still severely under-researched compared to monetary and "fiscal policy. We use the general framework of sequential predictions, also called online machine learning, to forecast crises out-of-sample. Our methodology is based on model aggregation and is “meta-statistical”, since we can incorporate any predictive model of crises in our analysis and test its ability to add information, without making any assumption on the data generating process. We predict systemic "financial crises twelve quarters ahead out-of-sample with high signal-to-noise ratio. Our approach guarantees that picking certain time dependent sets of weights will be asymptotically similar for out-of-sample forecasts to the best ex post combination of models; it also guarantees that we outperform any individual forecasting model asymptotically. We analyse which models provide the most information for our predictions at each point in time and for each country, allowing us to gain some insights into economic mechanisms underlying the building of risk in economies. |
| 主题 | Financial Economics ; Financial Markets |
| URL | https://www.nber.org/papers/w28302 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/585975 |
| 推荐引用方式 GB/T 7714 | Jeremy Fouliard,Michael Howell,Hélène Rey. Answering the Queen: Machine Learning and Financial Crises. 2021. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w28302.pdf（6015KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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。
