Sequential Monte Carlo samplers for Bayesian DSGE models

Bayesian estimation of DSGE models typically uses Markov chain Monte Carlo as importance sampling (IS) algorithms have a difficult time in high-dimensional spaces. I develop improved IS algorithms for DSGE models using recent advances in Monte Carlo methods known as sequential Monte Carlo samplers. Sequential Monte Carlo samplers are a generalization of particle filtering designed for full simulation of all parameters from the posterior. I build two separate algorithms; one for sequential Bayesian estimation and a second which performs batch estimation. The sequential Bayesian algorithm provides a new method for inspecting the time variation and stability of DSGE models. The posterior distribution of the DSGE model considered here changes substantially over time. In addition, the algorithm is a method for implementing Bayesian updating of the posterior.