Over-Dispersed Age-Period-Cohort Models

We consider inference and forecasting for aggregate data organized in a two-way table with age and cohort as indices, but without measures of exposure. This is modeled using a Poisson likelihood with an age-period-cohort structure for the mean while allowing for over-dispersion. We propose a repetitive structure that keeps the dimension of the table fixed while increasing the latent exposure. For this, we use a class of infinitely divisible distributions which include a variety of compound Poisson models and Poisson mixture models. This results in asymptotic <i>F</i> inference and <i>t</i> forecast distributions.