Business Cycle Analysis

We propose a flexible prior for vectorautoregressions (VARs) which can exploit the panel structure of macroeconomic time series and at the same time provide shrinkage towards zero in order to address overfitting concerns. The prior is flexible as it allows for parameter pooling across both a country dimension (two countries are completely alike) and/or variable dimension (dynamics of two variables across countries are alike). The usefulness of our approach is demonstrated via a Monte Carlo study and an empirical application using a large euro area data set. We find that cross country information helps deliver sharper parameter inference that improves point and density forecasts as well as structural analysis through lower estimation uncertainty. Also it is beneficial to have both pooling and shrinkage instead of only pooling.

In this article we address the question of how strongly bank lending rates and credit volumes co-move across the euro area. Following the breakdown in the interest rate pass-through across the euro area, we aim to disentangle the relative importance of country-specific and common components in explaining the variance of the macro and financial variables by using a time-varying two-level dynamic factor model. Our results show that a high share is explained by the common component. However, we find a persistent decline in the importance of the common factor in the bank lending rates, indicating the presence of financial fragmentation. Furthermore, we find persistent heterogeneity across member states, specifically those hit hard by the sovereign-debt crisis.