Stochastic model specification in Markov switching vector error correction models

This paper proposes a hierarchical modeling approach to perform stochastic model specification in Markovswitching vector error correction models. We assume that a common distribution gives rise to the regime-specific regression coefficients. The mean as well as the variances of this distribution are treated as fully stochas-tic and suitable shrinkage priors are used. These shrinkage priors enable to assess which coefficients differacross regimes in a flexible manner. In the case of similar coefficients, our model pushes the respective regionsof the parameter space towards the common distribution. This allows for selecting a parsimonious model whilestill maintaining sufficient flexibility to control for sudden shifts in the parameters, if necessary. We apply ourmodeling approach to real-time Euro area data and assume transition probabilities between expansionary andrecessionary regimes to be driven by the cointegration errors. The results suggest that the regime allocationis governed by a subset of short-run adjustment coefficients and regime-specific variance-covariance matri-ces. These findings are complemented by an out-of-sample forecast exercise, illustrating the advantages of themodel for predicting Euro area inflation in real time.