An application of the Autoregressive Conditional Poisson (ACP) model

Abstract

When modelling count data that comes in the form of a time series, the static Poisson regression and standard time series models are often not appropriate. A current study therefore involves the evaluation of several observation-driven and parameter-driven time series models for count data. In the observation-driven class of models, a fairly simple model is the Autoregressive Conditional Poisson (ACP) model. This presentation will describe the formulation of this model, together with the extension to the Double Autoregressive Conditional Poisson (DACP) model and also present some results of how these models compare to the static Poisson regression when modelling example data of cholera epidemics.