关键词: 动态随机一般均衡模型, 多重尝试Metropolis算法, 粒子滤波器, 贝叶斯因子

Abstract: This paper proposes a multiple-try-based Metropolis algorithm for Bayesian parameter estimation and model selection in nonlinear dynamic stochastic general equilibrium (DSGE) models. Multiple-try method generates multiple trial points in each iteration to achieve faster mixing rate. The new approach proposes to use approximation to save computational cost, the approximation bias is adjusted through the acceptance rate. Simulation results demonstrate effectiveness of the proposed algorithm. The algorithm is applied to estimate DSGE models with different monetary policy settings using the macroeconomic data of China. It shows that the data favors the model with time-varying inflation target.

Key words: DSGE Models, Multiple-try Metropolis Algorithm, Particle Filter, Bayes Factor