  This paper proposes a hierarchical modeling approach to perform stochastic
model specification in Markov switching 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 stochastic and suitable shrinkage priors are used. These
shrinkage priors enable to assess which coefficients differ across regimes in a
flexible manner. In the case of similar coefficients, our model pushes the
respective regions of the parameter space towards the common distribution. This
allows for selecting a parsimonious model while still maintaining sufficient
flexibility to control for sudden shifts in the parameters, if necessary. We
apply our modeling approach to real-time Euro area data and assume transition
probabilities between expansionary and recessionary regimes to be driven by the
cointegration errors. The results suggest that the regime allocation is
governed by a subset of short-run adjustment coefficients and regime-specific
variance-covariance matrices. These findings are complemented by an
out-of-sample forecast exercise, illustrating the advantages of the model for
predicting Euro area inflation in real time.

Econometrics