Conditional Prior Proposals in Dynamic Models

Dynamic models extend state space models to non-normal observations. This paper suggests a specific hybrid Metropolis-Hastings algorithm as a simple, yet flexible and efficient tool for Bayesian inference via Markov chain Monte Carlo in dynamic models. Hastings proposals from the (conditional) prior distribution of the unknown, time-varying parameters are used to update the corresponding full conditional distributions. Several blocking strategies are discussed to ensure good mixing and convergence properties of the simulated Markov chain. It is also shown that the proposed method is easily extended to robust transition models using mixtures of normals. The applicability is illustrated with an analysis of a binomial and a binary time series, known in the literature