| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP16496 |
| DP16496 Nowcasting Tail Risk to Economic Activity at a Weekly Frequency | |
| Massimiliano Marcellino; Todd Clark; Andrea Carriero | |
| 发表日期 | 2021-08-31 |
| 出版年 | 2021 |
| 语种 | 英语 |
| 摘要 | This paper focuses on nowcasts of tail risk to GDP growth, with a potentially wide array of monthly and weekly information used to produce nowcasts on a weekly basis. We consider different models, consisting of Bayesian mixed frequency regressions with stochastic volatility, Bayesian quantile regressions, and Bayesian partial quantile regression, the last of which incorporates data reduction through a common factor. Our results show that, within some limits, more information helps the accuracy of nowcasts of tail risk to GDP growth. Accuracy typically improves as time moves forward within a quarter, making additional data available, with monthly data more important to accuracy than weekly data. Accuracy also typically improves with the use of financial indicators in addition to a base set of macroeconomic indicators. |
| 主题 | Monetary Economics and Fluctuations |
| 关键词 | Forecasting Downside risk Pandemics Big data Mixed frequency Quantile regression |
| URL | https://cepr.org/publications/dp16496 |
| 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/545451 |
| 推荐引用方式 GB/T 7714 | Massimiliano Marcellino,Todd Clark,Andrea Carriero. DP16496 Nowcasting Tail Risk to Economic Activity at a Weekly Frequency. 2021. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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