Large Bayesian VARs for Binary and Censored Variables

Abstract

We extend the standard VAR to jointly model the dynamics of binary, censored and continuous variables, and develop an efficient estimation approach that scales well to high-dimensional settings. In an out-of-sample forecasting exercise, we show that the proposed VARs forecast recessions and short-term interest rates well. We demonstrate the utility of the proposed framework using a wide rage of empirical applications, including conditional forecasting and a structural analysis that examines the dynamic effects of a financial shock on recession probabilities.