SEQUENTIAL MONTE CARLO SAMPLING FOR DSGE MODELS
开发了一种序贯蒙特卡洛算法用于估计贝叶斯DSGE模型，通过逐步调整似然函数逼近后验分布。在Smets-Wouters模型和新闻冲击模型上，该算法比随机游走Metropolis-Hastings算法更擅长处理多峰和不规则后验分布，且适合并行计算。
SUMMARY 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 two empirical illustrations consisting of the Smets and Wouters model and a larger 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 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. Copyright © 2014 John Wiley & Sons, Ltd.