| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w13414 |
| 来源ID | Working Paper 13414 |
| Bayesian and Adaptive Optimal Policy under Model Uncertainty | |
| Lars E.O. Svensson; Noah M. Williams | |
| 发表日期 | 2007-09-14 |
| 出版年 | 2007 |
| 语种 | 英语 |
| 摘要 | We study the problem of a policymaker who seeks to set policy optimally in an economy where the true economic structure is unobserved, and he optimally learns from observations of the economy. This is a classic problem of learning and control, variants of which have been studied in the past, but seldom with forward-looking variables which are a key component of modern policy-relevant models. As in most Bayesian learning problems, the optimal policy typically includes an experimentation component reflecting the endogeneity of information. We develop algorithms to solve numerically for the Bayesian optimal policy (BOP). However, computing the BOP is only feasible in relatively small models, and thus we also consider a simpler specification we term adaptive optimal policy (AOP) which allows policymakers to update their beliefs but shortcuts the experimentation motive. In our setting, the AOP is significantly easier to compute, and in many cases provides a good approximation to the BOP. We provide some simple examples to illustrate the role of learning and experimentation in an MJLQ framework. |
| 主题 | Microeconomics ; Economics of Information ; Macroeconomics ; Money and Interest Rates ; Monetary Policy |
| URL | https://www.nber.org/papers/w13414 |
| 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/571085 |
| 推荐引用方式 GB/T 7714 | Lars E.O. Svensson,Noah M. Williams. Bayesian and Adaptive Optimal Policy under Model Uncertainty. 2007. |
| 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 | ||
| w13414.pdf（686KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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