| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w27014 |
| 来源ID | Working Paper 27014 |
| Advances in Structural Vector Autoregressions with Imperfect Identifying Information | |
| Christiane Baumeister; James D. Hamilton | |
| 发表日期 | 2020-04-20 |
| 出版年 | 2020 |
| 语种 | 英语 |
| 摘要 | This paper examines methods for structural interpretation of vector autoregressions when the identifying information is regarded as imperfect or incomplete. We suggest that a Bayesian approach offers a unifying theme for guiding inference in such settings. Among other advantages, the unified approach solves a problem with calculating elasticities that appears not to have been recognized by earlier researchers. We also call attention to some computational concerns of which researchers who approach this problem using other methods should be aware. |
| 主题 | Econometrics ; Estimation Methods ; Environmental and Resource Economics ; Energy |
| URL | https://www.nber.org/papers/w27014 |
| 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/584686 |
| 推荐引用方式 GB/T 7714 | Christiane Baumeister,James D. Hamilton. Advances in Structural Vector Autoregressions with Imperfect Identifying Information. 2020. |
| 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 | ||
| w27014.pdf（300KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
