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来源类型Working Paper
规范类型报告
DOI10.3386/w19152
来源IDWorking 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
URLhttps://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.
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