  In this paper we propose a new class of Dynamic Mixture Models (DAMMs) being
able to sequentially adapt the mixture components as well as the mixture
composition using information coming from the data. The information driven
nature of the proposed class of models allows to exactly compute the full
likelihood and to avoid computer intensive simulation schemes. An extensive
Monte Carlo experiment reveals that the new proposed model can accurately
approximate the more complicated Stochastic Dynamic Mixture Model previously
introduced in the literature as well as other kind of models. The properties of
the new proposed class of models are discussed through the paper and an
application in financial econometrics is reported.
