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How Well Do Markov Switching Models Describe Actual Business Cycles? The Case of Synchronization

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  • Penelope A. Smith

    (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne)

  • Peter M. Summers

    (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne)

Abstract

The objective of this paper is to evaluate the effectiveness of using a Markov switching model to measure the synchronization of business cycles. We use a Bayesian, Gibbs sampling approach to estimate a multivariate Markov switching model of GDP growth for several countries. We look for evidence of synchronization across countries in the sense of common Markov states, covariance of impulses and a long-run co-integrating relationship. We then use the fitted data implied by the posterior distribution of the Markov switching VAR, in conjunction with a dating rule, to obtain the posterior distribution of binary business cycle states. We use these to investigate the posterior distributions of non-parametric measures of synchronization described by Harding and Pagan (2003) and compare them with similar measures obtained from standard reference chronologies. As a point of reference, we repeat this exercise using simulated data from a linear VAR. We find no evidence of a common Markov state, but some evidence of the propagation of country-specific disturbances across countries and of a co-integrating relationship between the United States and Canada. Posterior odds ratios overwhelmingly favor the Markov switching model over the linear VAR and we find that the posterior distributions of the non-parametric measures of synchronisation produced by the Markov switching VAR match the data more closely than those produced by the linear VAR.

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Bibliographic Info

Paper provided by Melbourne Institute of Applied Economic and Social Research, The University of Melbourne in its series Melbourne Institute Working Paper Series with number wp2004n09.

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Length: 42 pages
Date of creation: May 2004
Date of revision:
Handle: RePEc:iae:iaewps:wp2004n09

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  1. Smith Penelope & Summers Peter M, 2009. "Regime Switches in GDP Growth and Volatility: Some International Evidence and Implications for Modeling Business Cycles," The B.E. Journal of Macroeconomics, De Gruyter, De Gruyter, vol. 9(1), pages 1-19, September.
  2. Michael D. Bordo & Thomas Helbling, 2003. "Have National Business Cycles Become More Synchronized?," NBER Working Papers 10130, National Bureau of Economic Research, Inc.
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  17. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, December.
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  20. Hess, Gregory D & Iwata, Shigeru, 1997. "Measuring and Comparing Business-Cycle Features," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 15(4), pages 432-44, October.
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Cited by:
  1. Chevallier, Julien, 2011. "Evaluating the carbon-macroeconomy relationship: Evidence from threshold vector error-correction and Markov-switching VAR models," Economics Papers from University Paris Dauphine 123456789/6970, Paris Dauphine University.
  2. Bilgili, Faik & Tülüce, Nadide Sevil Halıcı & Doğan, İbrahim, 2012. "The determinants of FDI in Turkey: A Markov Regime-Switching approach," Economic Modelling, Elsevier, Elsevier, vol. 29(4), pages 1161-1169.
  3. Jiang, Chun & Li, Xiao-Lin & Chang, Hsu-Ling & Su, Chi-Wei, 2013. "Uncovered interest parity and risk premium convergence in Central and Eastern European countries," Economic Modelling, Elsevier, Elsevier, vol. 33(C), pages 204-208.
  4. Leiva-Leon, Danilo, 2013. "A New Approach to Infer Changes in the Synchronization of Business Cycle Phases," MPRA Paper 54452, University Library of Munich, Germany.

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