| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP14603 |
| DP14603 Advances in Structural Vector Autoregressions with Imperfect Identifying Information | |
| Christiane Baumeister; James Hamilton | |
| 发表日期 | 2020-04-12 |
| 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 | International Macroeconomics and Finance ; Monetary Economics and Fluctuations |
| 关键词 | Structural vector autoregressions Bayesian analysis Identification Elasticities Sign restrictions Proxy vars |
| URL | https://cepr.org/publications/dp14603 |
| 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/543510 |
| 推荐引用方式 GB/T 7714 | Christiane Baumeister,James Hamilton. DP14603 Advances in Structural Vector Autoregressions with Imperfect Identifying Information. 2020. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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