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Convergence in European GDP Series

Author

Listed:
  • Rob Luginbuhl
  • Siem Jan Koopman

    () (Faculty of Economics and Business Administration, Vrije Universiteit Amsterdam)

Abstract

This discussion paper led to a publication in the Journal of Applied Econometrics . Vol. 19, issue 5, pages 611-636. Convergence in gross domestic product series of five European countriesis empirically identified using multivariate time series models that arebased on unobserved components with dynamic converging properties.We define convergence in terms of a decrease in dispersion over timeand model this decrease via mechanisms that allow for gradualreductions in the ranks of covariance matrices associated with thedisturbance vectors driving the unobserved components of the model.The inclusion of such convergence mechanisms within the formulation ofunobserved components makes the identification of various types ofconvergence possible.The common converging component model isestimated for the per capita gross domestic product of five Europeancountries: Germany, France, Italy, Spain and the Netherlands. It is foundthat convergence features in trends and cycles are present and areassociated with some key events in the history of European integration.

Suggested Citation

  • Rob Luginbuhl & Siem Jan Koopman, 2003. "Convergence in European GDP Series," Tinbergen Institute Discussion Papers 03-031/4, Tinbergen Institute.
  • Handle: RePEc:tin:wpaper:20030031
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    File URL: https://papers.tinbergen.nl/03031.pdf
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    References listed on IDEAS

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    3. 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.
    4. 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.
    5. Bernard, Andrew B. & Durlauf, Steven N., 1996. "Interpreting tests of the convergence hypothesis," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 161-173.
    6. Galor, Oded, 1996. "Convergence? Inferences from Theoretical Models," Economic Journal, Royal Economic Society, vol. 106(437), pages 1056-1069, July.
    7. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    8. Harvey, A C & Jaeger, A, 1993. "Detrending, Stylized Facts and the Business Cycle," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(3), pages 231-247, July-Sept.
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    Citations

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    Cited by:

    1. Willie Lahari, 2011. "Assessing Business Cycle Synchronisation - Prospects for a Pacific Islands Currency Union," Working Papers 1110, University of Otago, Department of Economics, revised Oct 2011.
    2. Leon, Costas, 2006. "The European and the Greek Business Cycles: Are they synchronized?," MPRA Paper 1312, University Library of Munich, Germany.
    3. Ossama Mikhail, 2004. "No More Rocking Horses: Trading Business-Cycle Depth for Duration Using an Economy-Specific Characteristic," Macroeconomics 0402026, University Library of Munich, Germany.

    More about this item

    Keywords

    Common trends and cycles; dynamic factor model; economic convergence; Kalman filter; multivariate unobserved components time series models;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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