Stochastic model specification in Markov switching vector error correction models

Niko Hauzenberger, Florian Huber, Michael Pfarrhofer, Thomas O. Zörner*

*Korrespondierende/r Autor/in für diese Arbeit

Publikation: Beitrag in FachzeitschriftArtikelPeer-reviewed

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.
OriginalspracheEnglisch
Aufsatznummer20180069
FachzeitschriftStudies in Nonlinear Dynamics and Econometrics
Jahrgang25
Ausgabenummer2
Frühes Online-Datum24 Feb 2020
DOIs
PublikationsstatusVeröffentlicht - 24 Feb 2021

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