  Detection and modeling of change-points in time-series can be considerably
challenging. In this paper we approach this problem by incorporating the class
of Dynamic Generalized Linear Models (DGLM) into the well know class of Product
Partition Models (PPM). This new methodology, that we call DGLM-PPM, extends
the PPM to distributions within the Exponential Family while also retaining the
flexibility of the DGLM class. It also provides a framework for Bayesian
multiple change-point detection in dynamic regression models. Inference on the
DGLM-PPM follow the steps of evolution and updating of the DGLM class. A Gibbs
Sampler scheme with an Adaptive Rejection Metropolis Sampling (ARMS) step
appended is used to compute posterior estimates of the relevant quantities. A
simulation study shows that the proposed model provides reasonable estimates of
the dynamic parameters and also assigns high change-point probabilities to the
breaks introduced in the artificial data generated for this work. We also
present a real life data example that highlights the superiority of the
DGLM-PPM over the conventional DGLM in both in-sample and out-of-sample
goodness of fit measures.
