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Business Cycle Analysis with Multivariate Markov Switching Models

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Author Info

  • Monica Billio

    ()
    (Department of Economics, University Of Venice Cà Foscari)

  • Jacques Anas

    (Coe Rexecode, Paris)

  • Laurent Ferrara

    (Banque de Frances)

  • Marco Lo Duca

    (European Central Bank)

Abstract

The class of Markov switching models can be extended in two main directions in a multivariate framework. In the first approach, the switching dynamics are introduced by way of a common latent factor. In the second approach a VAR model with parameters depending on one common Markov chain is considered (MSVAR). We will extend the MSVAR approach allowing for the presence of specific Markov chains in each equation of the VAR (MMSVAR). In the MMSVAR approach we also explore the introduction of correlated Markov chains which allow us to evaluate the relationships among phases in different economies or sectors and introduce causality relationships, which allow a more parsimonious representation. We apply our model to study the relationship between cyclical phases of the industrial production in the US and Euro zone. Moreover, we construct a MMS model to explore the cyclical relationship between the Euro zone industrial production and the industrial component of the European Sentiment Index.

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Bibliographic Info

Paper provided by Department of Economics, University of Venice "Ca' Foscari" in its series Working Papers with number 2007_32.

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Length: 31
Date of creation: 2007
Date of revision:
Handle: RePEc:ven:wpaper:2007_32

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Related research

Keywords: Economic cycles; Multivariate models; Markov switching models; Common latent factors; Causality; Euro-zone;

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References

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  1. Michael ARTIS & Massimiliano MARCELLINO & Tommaso PROIETTI, 2002. "Dating the Euro Area Business Cycle," Economics Working Papers ECO2002/24, European University Institute.
  2. Anas, Jacques & Ferrara, Laurent, 2002. "Un indicateur d'entrée et sortie de récession: application aux Etats-Unis
    [A start-end recession index: Application for United-States]
    ," MPRA Paper 4043, University Library of Munich, Germany.
  3. Diebold, Francis X & Rudebusch, Glenn D, 1996. "Measuring Business Cycles: A Modern Perspective," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 67-77, February.
  4. Monica Billio & Alain Monfort & Christian P, Robert, 1998. "The Simulated Likelihood Ratio (SLR) Method," Working Papers 98-21, Centre de Recherche en Economie et Statistique.
  5. Clements, Michael P & Krolzig, Hans-Martin, 2003. "Business Cycle Asymmetries: Characterization and Testing Based on Markov-Switching Autoregressions," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 196-211, January.
  6. Chauvet, Marcelle, 1998. "An Econometric Characterization of Business Cycle Dynamics with Factor Structure and Regime Switching," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 969-96, November.
  7. Monica Billio & Jacques Anas & Laurent Ferrara & Marco Lo Duca, 2007. "A turning point chronology for the Euro-zone," Working Papers 2007_33, Department of Economics, University of Venice "Ca' Foscari".
  8. Jacques Anas & Laurent Ferrara, 2004. "Detecting Cyclical Turning Points: The ABCD Approach and Two Probabilistic Indicators," Journal of Business Cycle Measurement and Analysis, OECD Publishing,CIRET, vol. 2004(2), pages 193-225.
  9. Robert Breunig & Serinah Najarian & Adrian Pagan, 2003. "Specification Testing of Markov Switching Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 703-725, December.
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Citations

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Cited by:
  1. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2012. "Combination schemes for turning point predictions," Working Paper 2012/04, Norges Bank.
  2. repec:hal:journl:halshs-00423890 is not listed on IDEAS
  3. Giampiero Gallo & Edoardo Otranto, 2007. "Volatility Spillovers, Interdependence and Comovements: A Markov Switching Approach," Econometrics Working Papers Archive wp2007_11, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
  4. Fady Barsoum & Sandra Stankiewicz, 2013. "Forecasting GDP Growth Using Mixed-Frequency Models With Switching Regimes," Working Paper Series of the Department of Economics, University of Konstanz 2013-10, Department of Economics, University of Konstanz.

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