| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w29495 |
| 来源ID | Working Paper 29495 |
| Learning About the Long Run | |
| Leland Farmer; Emi Nakamura; Jón Steinsson | |
| 发表日期 | 2021-11-22 |
| 出版年 | 2021 |
| 语种 | 英语 |
| 摘要 | Forecasts of professional forecasters are anomalous: they are biased, forecast errors are autocorrelated, and predictable by forecast revisions. Sticky or noisy information models seem like unlikely explanations for these anomalies: professional forecasters pay attention constantly and have precise knowledge of the data in question. We propose that these anomalies arise because professional forecasters don’t know the model that generates the data. We show that Bayesian agents learning about hard-to-learn features of the data generating process (low frequency behavior) can generate all the prominent aggregate anomalies emphasized in the literature. We show this for two applications: professional forecasts of nominal interest rates for the sample period 1980-2019 and CBO forecasts of GDP growth for the sample period 1976- 2019. Our learning model for interest rates also provides an explanation for deviations from the expectations hypothesis of the term structure that does not rely on time-variation in risk premia. |
| 主题 | Macroeconomics ; Business Cycles ; Money and Interest Rates ; Financial Economics ; Portfolio Selection and Asset Pricing |
| URL | https://www.nber.org/papers/w29495 |
| 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/587169 |
| 推荐引用方式 GB/T 7714 | Leland Farmer,Emi Nakamura,Jón Steinsson. Learning About the Long Run. 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 | ||
| w29495.pdf（1260KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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