| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w18467 |
| 来源ID | Working Paper 18467 |
| Prior Selection for Vector Autoregressions | |
| Domenico Giannone; Michele Lenza; Giorgio E. Primiceri | |
| 发表日期 | 2012-10-18 |
| 出版年 | 2012 |
| 语种 | 英语 |
| 摘要 | Vector autoregressions (VARs) are flexible time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, their dense parameterization leads to unstable inference and inaccurate out-of-sample forecasts, particularly for models with many variables. A solution to this problem is to use informative priors, in order to shrink the richly parameterized unrestricted model towards a parsimonious naïve benchmark, and thus reduce estimation uncertainty. This paper studies the optimal choice of the informativeness of these priors, which we treat as additional parameters, in the spirit of hierarchical modeling. This approach is theoretically grounded, easy to implement, and greatly reduces the number and importance of subjective choices in the setting of the prior. Moreover, it performs very well both in terms of out-of-sample forecasting--as well as factor models--and accuracy in the estimation of impulse response functions. |
| 主题 | Econometrics ; Estimation Methods ; Macroeconomics ; Business Cycles ; Money and Interest Rates |
| URL | https://www.nber.org/papers/w18467 |
| 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/576141 |
| 推荐引用方式 GB/T 7714 | Domenico Giannone,Michele Lenza,Giorgio E. Primiceri. Prior Selection for Vector Autoregressions. 2012. |
| 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 | ||
| w18467.pdf（306KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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