| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP15854 |
| DP15854 Nowcasting with Large Bayesian Vector Autoregressions | |
| Jacopo Cimadomo; Domenico Giannone; Michele Lenza; Francesca Monti; Andrej Sokol | |
| 发表日期 | 2021-02-26 |
| 出版年 | 2021 |
| 语种 | 英语 |
| 摘要 | Monitoring economic conditions in real time, or nowcasting, and Big Data analytics share some challenges, sometimes called the three "Vs". Indeed, nowcasting is characterized by the use of a large number of time series (Volume), the complexity of the data covering various sectors of the economy, with different frequencies and precision and asynchronous release dates (Variety), and the need to incorporate new information continuously and in a timely manner (Velocity). In this paper, we explore three alternative routes to nowcasting with Bayesian Vector Autoregressive (BVAR) models and find that they can effectively handle the three Vs by producing, in real time, accurate probabilistic predictions of US economic activity and a meaningful narrative by means of scenario analysis. |
| 主题 | Monetary Economics and Fluctuations |
| 关键词 | Big data Scenario analysis Mixed frequency Real time Business cycles Nowcasting |
| URL | https://cepr.org/publications/dp15854 |
| 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/544847 |
| 推荐引用方式 GB/T 7714 | Jacopo Cimadomo,Domenico Giannone,Michele Lenza,et al. DP15854 Nowcasting with Large Bayesian Vector Autoregressions. 2021. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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