Efficient Parameter-Expanded Gibbs Samplers for Dynamic Factor Models

Abstract

A dynamic factor model is estimated with a parameter-expanded algorithm to accelerate standard Gibbs sampling. The proposed algorithm is easy to implement since it involves only draws from standard distributions. It also leads to substantial improvement in the Markov chain Monte Carlo (MCMC) performance compared to prevailing