Improving inference and forecasting in VAR models using cross-sectional information
We propose a prior for VAR models that exploits the panel structure of macroeconomic time series while also providing shrinkage towards zero to address overfitting concerns. The prior is flexible as it detects shared dynamics of individual variables across endogenously determined groups of countries. We demonstrate the usefulness of our approach via a Monte Carlo study and use our model to capture the hidden homo- and heterogeneities of the euro area member states. Combining pairwise pooling with zero shrinkage delivers sharper parameter inference that improves point and density forecasts over only zero shrinkage or only pooling specifications, and helps with structural analysis by lowering the estimation uncertainty.