Bayesian Inference of Multivariate Regression Models with Endogenous Markov Regime-Switching Parameters

Young Min Kim, Kyu Ho Kang

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


This study introduces a multivariate regression model with endogenous Markov regime-switching parameters, in which the regression disturbances and regime switches are allowed to be instantaneously correlated. For the estimation and model comparison, we develop a posterior sampling algorithm for the parameters, regimes, and marginal likelihood calculation. We demonstrate the reliability of the proposed method using simulation and empirical studies. The simulation study shows that neglecting the endogeneity leads to inaccurate parameter estimates, and that our marginal likelihood comparison chooses a correctly specified model. In the business cycle application, we find that the joint dynamics of the U.S. industrial production index (IPI) growth and unemployment rates are subject to three-state endogenous regime shifts. Another application to stock and bond return data suggests that negative shocks to the stock return seem to cause regime shifts from a low volatility state to a high volatility state of the financial markets. (JEL: C11, C53, E43, G12).

Original languageEnglish
Pages (from-to)391-436
Number of pages46
JournalJournal of Financial Econometrics
Issue number3
Publication statusPublished - 2022

Bibliographical note

Publisher Copyright:
© 2020 The Author(s) 2020. Published by Oxford University Press. All rights reserved.


  • auxiliary variable
  • Bayesian MCMC estimation
  • financial markets
  • marginal likelihood
  • U.S. business cycle

ASJC Scopus subject areas

  • Finance
  • Economics and Econometrics


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