Bayesian inference in a simultaneous equation model with limited dependent variables

This paper develops a Bayesian approach for inference in a simultaneous equation model with limited dependent variables (SLDV). By employing a combination of Gibbs sampling and data augmentation, we can draw from the exact posterior of this SLDV model and avoid direct evaluation of the non-trivial likelihood function. A by-product from our posterior simulation is the Savage—Dickey density ratio which is used for computing the Bayes factor. The practicality and efficiency of the proposed method are illustrated through an example in corporate finance.