MCMC and Particle Filtering for Dynamic INAR Processes

In this article, we present a dynamic version of the integer autoregressive (INAR) processes for count data. The proposed Bayesian model provides a unification of the previously considered models to describe temporal correlations in univariate time series of counts. We develop Bayesian inference for the proposed class of models via MCMC and introduce a particle filtering (PF) algorithm for sequential inference. The new class of models are compared with their static counterparts using actual count series and additional insights provided by the new models are discussed.