Macroeconomic and credit forecasts during the Greek crisis using Bayesian VARs

Abstract

We investigate the ability of small- and medium-scale Bayesian VARs (BVARs) to produce accurate macroeconomic (output and inflation) and credit (loans and lending rate) out-of-sample forecasts during the latest Greek crisis. We implement recently proposed Bayesian shrinkage techniques based on Bayesian hierarchical modeling, and we evaluate the information content of forty-two (42) monthly macroeconomic and financial variables in terms of point and density forecasting. Alternative competing models employed in the study include Bayesian autoregressions (BARs) and time-varying parameter VARs with stochastic volatility, among others. The empirical results reveal that, overall, medium-scale BVARs enriched with economy-wide variables can considerably and consistently improve short-term inflation forecasts. The information content of financial variables, on the other hand, proves to be beneficial for the lending rate density forecasts across forecasting horizons. Both of the above-mentioned results are robust to alternative specification choices, while for the rest of the variables smaller-scale BVARs, or even univariate BARs, produce superior forecasts. Finally, we find that the popular, data-driven, shrinkage methods produce, on average, inferior forecasts compared to the theoretically grounded method considered here.