A BAYESIAN ANALYSIS OF ENDOGENOUS SWITCHING MODELS FOR COUNT DATA

Abstract

This paper considers the count model with endogenous switching proposed by Terza (1998) from a Bayesian point of view. We consider Markov chain Monte Carlo methods to estimate the parameters of the model. Furthermore, an extension is made to handle the case of non-normality and model determination is discussed. Our approach is illustrated with both simulated and real data sets.