Barra, I., Hoogerheide, L., Koopman, S., and Lucas, A. (2017). Joint Bayesian analysis of parameters and states in nonlinear, non-gaussian state space models. Journal of Applied Econometrics, 32(5):1003-1026.
JournalJournal of Applied Econometrics
We propose a new methodology for designing flexible proposal densities for the joint posterior density of parameters and states in a nonlinear, non-Gaussian state space model. We show that a highly efficient Bayesian procedure emerges when these proposal densities are used in an independent Metropolis-Hastings algorithm or in importance sampling. Our method provides a computationally more efficient alternative to several recently proposed algorithms. We present extensive simulation evidence for stochastic intensity and stochastic volatility models based on Ornstein-Uhlenbeck processes. For our empirical study, we analyse the performance of our methods for corporate default panel data and stock index returns.