Predictions in overdispersed series of counts using an approximate predictive likelihood

The generalized autoregression model or GARM, originally used to model series of non-negative data measured at irregularly spaced time points, is considered in a count data context. It is first shown how the GARM can be expressed as a generalized linear model in the special case of a linear model for some transform of the location parameter. The Butler approximate predictive likelihood is then used to define likelihood prediction envelopes. The width of these intervals is shown to be slightly wider than the Fisher, and Lejeune and Faulkenberry predictive likelihood-based envelopes which assume that the parameters have fixed known values (equal to their maximum likelihood estimates). The method is illustrated on a small count data set showing overdispersion.