A sequential Monte Carlo approach to inference in multiple-equation Markov-switching models

Vector autoregressions with Markov-switching parameters (MS-VARs) offer substantial gains in data fit over VARs with constant parameters. However, Bayesian inference for MS-VARs has remained challenging, impeding their uptake for empirical applications. We show that sequential Monte Carlo (SMC) estimators can accurately estimate MS-VAR posteriors. Relative to multi-step, model-specific MCMC routines, SMC has the advantages of generality, parallelizability, and freedom from reliance on particular analytical relationships between prior and likelihood. We use SMC’s flexibility to demonstrate that model selection among MS-VARs can be highly sensitive to the choice of prior.