| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP17133 |
| DP17133 Identifying Monetary Policy Shocks: A Natural Language Approach | |
| Thomas Drechsel; Borağan Aruoba | |
| 发表日期 | 2022-03-24 |
| 出版年 | 2022 |
| 语种 | 英语 |
| 摘要 | We propose a novel method for the identification of monetary policy shocks. By applying natural language processing techniques to documents that economists at the Federal Reserve prepare for Federal Open Market Committee meetings, we capture the information set available to the committee at the time of policy decisions. Using machine learning techniques, we then predict changes in the target interest rate conditional on this information set, and obtain a measure of monetary policy shocks as the residual. An appealing feature of our procedure is that only a small fraction of interest rate changes is attributed to exogenous shocks. We find that the dynamic responses of macroeconomic variables to our identified shocks are consistent with the theoretical consensus. |
| 主题 | International Macroeconomics and Finance ; Monetary Economics and Fluctuations |
| 关键词 | monetary policy Federal Reserve Natural language processing Machine learning |
| URL | https://cepr.org/publications/dp17133-3 |
| 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/546117 |
| 推荐引用方式 GB/T 7714 | Thomas Drechsel,Borağan Aruoba. DP17133 Identifying Monetary Policy Shocks: A Natural Language Approach. 2022. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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