| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP9931 |
| DP9931 Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections | |
| Domenico Giannone; Marta Banbura; Michele Lenza | |
| 发表日期 | 2014-04-13 |
| 出版年 | 2014 |
| 语种 | 英语 |
| 摘要 | This paper describes an algorithm to compute the distribution of conditional forecasts, i.e. projections of a set of variables of interest on future paths of some other variables, in dynamic systems. The algorithm is based on Kalman filtering methods and is computationally viable for large vector autoregressions (VAR) and dynamic factor models (DFM). For a quarterly data set of 26 euro area macroeconomic and financial indicators, we show that both approaches deliver similar forecasts and scenario assessments. In addition, conditional forecasts shed light on the stability of the dynamic relationships in the euro area during the recent episodes of financial turmoil and indicate that only a small number of sources drive the bulk of the fluctuations in the euro area economy. |
| 主题 | International Macroeconomics |
| 关键词 | Bayesian shrinkage Conditional forecast Dynamic factor model Large cross-sections Vector autoregression |
| URL | https://cepr.org/publications/dp9931 |
| 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/538765 |
| 推荐引用方式 GB/T 7714 | Domenico Giannone,Marta Banbura,Michele Lenza. DP9931 Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections. 2014. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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