Stochastic model specification in Markov switching vector error correction models

TL;DR

This paper introduces a hierarchical Bayesian approach for stochastic model specification in Markov switching vector error correction models, allowing flexible regime-specific parameter estimation and improved real-time inflation forecasting.

Contribution

It develops a novel hierarchical modeling framework with shrinkage priors for regime-specific coefficients in Markov switching models, enhancing model flexibility and interpretability.

Findings

Regime allocation driven by subset of short-run adjustment coefficients

Model effectively captures regime-specific variance-covariance matrices

Improves real-time inflation prediction accuracy

Abstract

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.