BAYESIAN-INFERENCE IN A SIMULTANEOUS EQUATION MODEL WITH LIMITED DEPENDENT-VARIABLES

This paper develops a Bayesian approach for inference in a simultaneou s equation model with limited dependent variables (SLDV). By employing a combination of Gibbs sampling and data augmentation, we can draw fr om the exact posterior of this SLDV model and avoid direct evaluation of the non-trivial likelihood function. A by-product from our posterio r simulation is the Savage-Dickey density ratio which is used for comp uting the Bayes factor, The practicality and efficiency of the propose d method are illustrated through an example in corporate finance. (C) 1998 Elsevier Science S.A. All rights reserved.