| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w26826 |
| 来源ID | Working Paper 26826 |
| Online Estimation of DSGE Models | |
| Michael D. Cai; Marco Del Negro; Edward P. Herbst; Ethan Matlin; Reca Sarfati; Frank Schorfheide | |
| 发表日期 | 2020-03-09 |
| 出版年 | 2020 |
| 语种 | 英语 |
| 摘要 | This paper illustrates the usefulness of sequential Monte Carlo (SMC) methods in approximating DSGE model posterior distributions. We show how the tempering schedule can be chosen adaptively, document the accuracy and runtime benefits of generalized data tempering for “online” estimation (that is, re-estimating a model as new data become available), and provide examples of multimodal posteriors that are well captured by SMC methods. We then use the online estimation of the DSGE model to compute pseudo-out-of-sample density forecasts and study the sensitivity of the predictive performance to changes in the prior distribution. We find that making priors less informative (compared to the benchmark priors used in the literature) by increasing the prior variance does not lead to a deterioration of forecast accuracy. |
| 主题 | Econometrics ; Estimation Methods ; Macroeconomics ; Business Cycles ; Monetary Policy |
| URL | https://www.nber.org/papers/w26826 |
| 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/584499 |
| 推荐引用方式 GB/T 7714 | Michael D. Cai,Marco Del Negro,Edward P. Herbst,et al. Online Estimation of DSGE Models. 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 | ||
| w26826.pdf（840KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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