Dynamic models extend state space models to non-normal observations. This p
aper suggests a specific hybrid Metropolis-Hastings algorithm as a simple d
evice for Bayesian inference via Markov chain Monte Carlo in dynamic models
, Hastings proposals from the (conditional) prior distribution of the unkno
wn, time-varying parameters are used to update the corresponding full condi
tional distributions. It is shown through simulated examples that the metho
dology has optimal performance in situations where the prior is relatively
strong compared to the likelihood. Typical examples include smoothing prior
s for categorical data. A specific blocking strategy is proposed to ensure
good mixing and convergence properties of the simulated Markov chain. It is
also shown that the methodology is easily extended to robust transition mo
dels using mixtures of normals. The applicability is illustrated with an an
alysis of a binomial and a binary time series, known in the literature.