Macroeconomic and credit forecasts in a small economy during crisis: A large Bayesian VAR approach

We examine the ability of large-scale vector autoregressions (VARs) to produce accurate macroeconomic (output and inflation) and credit (loans and lending rates) forecasts in Greece, during the latest sovereign debt crisis. We implement recently proposed Bayesian shrinkage techniques and we evaluate the information content of forty two (42) monthly macroeconomic and financial variables in a large Bayesian VAR context, using a five year out-of-sample forecasting period from 2008 to 2013. The empirical results reveal that, overall, large-scale Bayesian VARs, enhanced with key financial variables and coupled with the appropriate level of shrinkage, outperform their small- and medium-scale counterparts with respect to both macroeconomic and credit variables. The forecasting superiority of large Bayesian VARs is particularily clear at long-term forecasting horizons. Finally, empirical evidence suggests that large Bayesian VARs can significantly improve the directional forecasting accuracy of small VARs with respect to loans and lending rates variables.