| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP16488 |
| DP16488 Robust Bayesian Analysis for Econometrics | |
| Raffaella Giacomini; Toru Kitagawa; Matthew Read | |
| 发表日期 | 2021-08-27 |
| 出版年 | 2021 |
| 语种 | 英语 |
| 摘要 | We review the literature on robust Bayesian analysis as a tool for global sensitivity analysis and for statistical decision-making under ambiguity. We discuss the methods proposed in the literature, including the different ways of constructing the set of priors that are the key input of the robust Bayesian analysis. We consider both a general set-up for Bayesian statistical decisions and inference and the special case of set-identified structural models. We provide new results that can be used to derive and compute the set of posterior moments for sensitivity analysis and to compute the optimal statistical decision under multiple priors. The paper ends with a self-contained discussion of three different approaches to robust Bayesian inference for set- identified structural vector autoregressions, including details about numerical implementation and an empirical illustration. |
| 主题 | Monetary Economics and Fluctuations |
| URL | https://cepr.org/publications/dp16488 |
| 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/545444 |
| 推荐引用方式 GB/T 7714 | Raffaella Giacomini,Toru Kitagawa,Matthew Read. DP16488 Robust Bayesian Analysis for Econometrics. 2021. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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