| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP17035 |
| DP17035 Sequential Monte Carlo With Model Tempering | |
| Marko Mlikota; Frank Schorfheide | |
| 发表日期 | 2022-02-15 |
| 出版年 | 2022 |
| 语种 | 英语 |
| 摘要 | Modern macroeconometrics often relies on time series models for which it is time-consuming to evaluate the likelihood function. We demonstrate how Bayesian computations for such models can be drastically accelerated by reweighting and mutating posterior draws from an approximating model that allows for fast likelihood evaluations, into posterior draws from the model of interest, using a sequential Monte Carlo (SMC) algorithm. We apply the technique to the estimation of a vector autoregression with stochastic volatility and a nonlinear dynamic stochastic general equilibrium model. The runtime reductions we obtain range from 27% to 88%. |
| 主题 | Monetary Economics and Fluctuations |
| 关键词 | Bayesian computations Dynamic stochastic general equilibrium models Sequential monte carlo stochastic volatility Vector autoregressions |
| URL | https://cepr.org/publications/dp17035 |
| 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/545989 |
| 推荐引用方式 GB/T 7714 | Marko Mlikota,Frank Schorfheide. DP17035 Sequential Monte Carlo With Model Tempering. 2022. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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