-run predictions of a wide class of theoretical models yields substantial improvements in the forecasting performance. …

shrinkage towards zero to address overfitting concerns. The prior is flexible as it detects shared dynamics of individual … 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. …

This paper provides an empirical comparison of various selection and penalized regression approaches for forecasting … various prior specification choices on the relative and overall forecasting performance of the methods. The data set is a … - depending on the employed shrinkage method. …

, estimated shrinkage, and no nonlinearity. Then I entertain alternative specifications of the zero lower bound: replace the …

shrinkage. We derive point and density forecasts for euro area real GDP growth and HICP inflation conditional on an information … cycle, and on the size of the dataset. Overall, we find that a factor augmented BVAR with shrinkage is competitive in all …

Recent articles suggest that a Bayesian vector autoregression (BVAR) with shrinkage is a good forecast device even when …), and large BVARs, which differ in the way information is condensed and shrinkage is implemented. We find that: (a) large … shows the opposite pattern; (c) BFAVARs perform well under both evaluation criteria; (d) choosing the degree of shrinkage …