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.