Auxiliary mixture sampling for parameter-driven models of time series of counts with applications to state space modelling
Department of Applied Statistics and Econometrics, Johannes Kepler Universität Linz, A-4040 Linz, Austria sylvia.fruehwirth-schnatter{at}jku.at, helga.wagner{at}jku.at
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Abstract |
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We consider parameter-driven models of time series of counts, where the observations are assumed to arise from a Poisson distribution with a mean changing over time according to a latent process. Estimation of these models is carried out within a Bayesian framework using data augmentation and Markov chain Monte Carlo methods. We suggest a new auxiliary mixture sampler, which possesses a Gibbsian transition kernel, where we draw from full conditional distributions belonging to standard distribution families only. Emphasis lies on application to state space modelling of time series of counts, but we show that auxiliary mixture sampling may be applied to a wider range of parameter-driven models, including random-effects models and panel data models based on the Poisson distribution.
Key Words: Count data; Data augmentation; Finite mixture approximation; Gibbs sampling; Partially Gaussian state space model.
Received May 2004. Revised May 2006.
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