Stochastic model specification in Markov switching vector error correction models

Activity: Talk or presentationScience to science

Description

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. In the empirical application, we apply our modeling approach to Euro area data and assume that transition probabilities between expansion and recession regimes are driven by the cointegration errors. Our findings suggest that lagged cointegration errors have predictive power for regime shifts and these movements between business cycle stages are mostly driven by differences in error variances.
Period11 Apr 201913 Apr 2019
Event title24th Spring Meeting of Young Economists (SMYE)
Event typeUnknown
Degree of RecognitionInternational

Austrian Classification of Fields of Science and Technology (ÖFOS)

  • 502018 Macroeconomics
  • 101026 Time series analysis
  • 502025 Econometrics
  • 502047 Economic theory