Bayesian Nonparametric Predictions for Count Time Series.
Antonio Carlos Canale, Luisa Bisaglia · 46TH SCIENTIFIC MEETING OF THE ITALIAN STATISTICAL SOCIETY · 2012
In this paper we introduce a Bayesian nonparametric methodology for producing coherent predictions of count time series using the INAR(1) process. Our predictions are based on estimates of the $p$-step ahead predictive mass functions assuming a nonparametric distribution for the error term having large support on the space of discrete probability. An efficient Gibbs sampler is developed for posterior computation.