| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w19152 |
| 来源ID | Working Paper 19152 |
| Sequential Monte Carlo Sampling for DSGE Models | |
| Edward P. Herbst; Frank Schorfheide | |
| 发表日期 | 2013-06-20 |
| 出版年 | 2013 |
| 语种 | 英语 |
| 摘要 | We develop a sequential Monte Carlo (SMC) algorithm for estimating Bayesian dynamic stochastic general equilibrium (DSGE) models, wherein a particle approximation to the posterior is built iteratively through tempering the likelihood. Using three examples consisting of an artificial state-space model, the Smets and Wouters (2007) model, and Schmitt-Grohé and Uribe's (2012) news shock model we show that the SMC algorithm is better suited for multimodal and irregular posterior distributions than the widely-used random walk Metropolis- Hastings algorithm. We find that a more diffuse prior for the Smets and Wouters (2007) model improves its marginal data density and that a slight modification of the prior for the news shock model leads to drastic changes in the posterior inference about the importance of news shocks for fluctuations in hours worked. Unlike standard Markov chain Monte Carlo (MCMC) techniques, the SMC algorithm is well suited for parallel computing. |
| 主题 | Econometrics ; Estimation Methods ; Macroeconomics ; Macroeconomic Models |
| URL | https://www.nber.org/papers/w19152 |
| 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/576826 |
| 推荐引用方式 GB/T 7714 | Edward P. Herbst,Frank Schorfheide. Sequential Monte Carlo Sampling for DSGE Models. 2013. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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