| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP17410 |
| DP17410 Measuring Brexit Uncertainty: A Machine Learning and Textual Analysis Approach | |
| Wanyu Chung; Duiyi Dai; Robert Elliott | |
| 发表日期 | 2022-06-27 |
| 出版年 | 2022 |
| 语种 | 英语 |
| 摘要 | In this paper we develop a series of Brexit uncertainty indices (BUI) based on UK newspaper coverage. Using unsupervised machine learning (ML) methods to automatically select topics, our main contribution is to generate timely and cost-effective indicators of uncertainty. In further analysis we are able to distinguish Brexit related uncertainty from the uncertainly due to COVID-19. Our indices can be used to investigate Brexit-related uncertainties across different policy areas. |
| 主题 | International Trade and Regional Economics |
| 关键词 | Brexit Uncertainty Machine learning |
| URL | https://cepr.org/publications/dp17410 |
| 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/546484 |
| 推荐引用方式 GB/T 7714 | Wanyu Chung,Duiyi Dai,Robert Elliott. DP17410 Measuring Brexit Uncertainty: A Machine Learning and Textual Analysis Approach. 2022. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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