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Convergence in European GDP series: a multivariate common converging trend-cycle decomposition

Author

Listed:
  • Rob Luginbuhl

    (Department of Econometrics, Free University, Amsterdam and Tinbergen Institute, Amsterdam, Netherlands)

  • Siem Jan Koopman

    (Department of Econometrics, Free University, Amsterdam and Tinbergen Institute, Amsterdam, Netherlands)

Abstract

Convergence in the gross domestic product series of five European countries is empirically identified using multivariate time series models that are based on unobserved components with dynamic converging properties. We define convergence in terms of a decrease in dispersion over time and model this decrease via mechanisms that allow for gradual reductions in the ranks of covariance matrices associated with the disturbance vectors driving the unobserved components of the model. The inclusion of such convergence mechanisms within the formulation of unobserved components makes the identification of various types of convergence possible. The common converging component model is estimated for the per capita gross domestic product of five European countries: Germany, France, Italy, Spain and the Netherlands. It is found that convergence features in trends and cycles are present and are associated with some key events in the history of European integration. Copyright © 2004 John Wiley & Sons, Ltd.

Suggested Citation

  • Rob Luginbuhl & Siem Jan Koopman, 2004. "Convergence in European GDP series: a multivariate common converging trend-cycle decomposition," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(5), pages 611-636.
  • Handle: RePEc:jae:japmet:v:19:y:2004:i:5:p:611-636
    DOI: 10.1002/jae.785
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    File URL: http://qed.econ.queensu.ca:80/jae/2004-v19.5/
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    References listed on IDEAS

    as
    1. David Cook, 2002. "World War II And Convergence," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 131-138, February.
    2. Knowles, Stephen, 2001. "Are the Penn World Tables data on government consumption and investment being misused?," Economics Letters, Elsevier, vol. 71(2), pages 293-298, May.
    3. Quah, Danny T, 1996. "Twin Peaks: Growth and Convergence in Models of Distribution Dynamics," Economic Journal, Royal Economic Society, vol. 106(437), pages 1045-1055, July.
    4. Bernard, Andrew B. & Durlauf, Steven N., 1996. "Interpreting tests of the convergence hypothesis," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 161-173.
    5. Galor, Oded, 1996. "Convergence? Inferences from Theoretical Models," Economic Journal, Royal Economic Society, vol. 106(437), pages 1056-1069, July.
    6. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    7. Bernard, Andrew B & Durlauf, Steven N, 1995. "Convergence in International Output," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(2), pages 97-108, April-Jun.
    8. Siem Jan Koopman & Neil Shephard & Jurgen A. Doornik, 1999. "Statistical algorithms for models in state space using SsfPack 2.2," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 107-160.
    9. Danny Quah, 1996. "Twin Peaks: Growth and Convergence in Models of Distribution Dynamics," CEP Discussion Papers dp0280, Centre for Economic Performance, LSE.
    10. Harvey, A. & Vasco Carvalho, 2002. "Models for Converging Economies," Cambridge Working Papers in Economics 0216, Faculty of Economics, University of Cambridge.
    11. Jurgen A. Doornik & Henrik Hansen, 2008. "An Omnibus Test for Univariate and Multivariate Normality," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(s1), pages 927-939, December.
    12. Jonathan Temple, 1999. "The New Growth Evidence," Journal of Economic Literature, American Economic Association, vol. 37(1), pages 112-156, March.
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