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We develop a Bayesian framework for estimation and prediction of dynamicmodels for observations from the two-parameter exponential family. Differentlink functions are introduced to model both the mean and the precision in theexponential family allowing the introduction of covariates and time seriescomponents. We explore conjugacy and analytical approximations under the classof partial specified models to keep the computation fast. The algorithm ofWest, Harrison and Migon (1985) is extended to cope with the two-parameterexponential family models. The methodological novelties are illustrated withtwo applications to real data. The first, considers unemployment rates inBrazil and the second some macroeconomic variables for the United Kingdom.

Extended Dynamic Generalized Linear Models: the two-parameter exponential family