Introduction to Bayesian VAR estimation in Stata

The use of the Bayesian approach for regression analysis is spreading more across different disciplines. The possibility to incorporate a priori information in the form of probability distributions for the parameters of the model makes this approach highly appealing when the researcher has that knowledge. Bayesian vector autoregressive models (BVAR) are particularly attractive because the overparameterization present in many VAR models can be handled by using prior probability distributions that allow shrinking the parameter space. In this presentation, I will briefly highlight the general elements associated with Bayesian VAR models, and I will use a couple of examples to illustrate the way Stata implements the estimation for the parameters of a VAR model using the Bayesian approach and how we can get probabilities for events that combine levels for the different endogenous variables of the model.