| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w23448 |
| 来源ID | Working Paper 23448 |
| Tempered Particle Filtering | |
| Edward Herbst; Frank Schorfheide | |
| 发表日期 | 2017-05-29 |
| 出版年 | 2017 |
| 语种 | 英语 |
| 摘要 | The accuracy of particle filters for nonlinear state-space models crucially depends on the proposal distribution that mutates time t-1 particle values into time t values. In the widely-used bootstrap particle filter, this distribution is generated by the state-transition equation. While straightforward to implement, the practical performance is often poor. We develop a self-tuning particle filter in which the proposal distribution is constructed adaptively through a sequence of Monte Carlo steps. Intuitively, we start from a measurement error distribution with an inflated variance, and then gradually reduce the variance to its nominal level in a sequence of tempering steps. We show that the filter generates an unbiased and consistent approximation of the likelihood function. Holding the run time fixed, our filter is substantially more accurate in two DSGE model applications than the bootstrap particle filter. |
| 主题 | Econometrics ; Estimation Methods ; Macroeconomics ; Business Cycles |
| URL | https://www.nber.org/papers/w23448 |
| 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/581121 |
| 推荐引用方式 GB/T 7714 | Edward Herbst,Frank Schorfheide. Tempered Particle Filtering. 2017. |
| 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 | ||
| w23448.pdf（806KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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