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New Composite Leading Indicators for Hungary and Poland

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  • Harm Bandholz
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    Abstract

    This paper presents new composite leading indicators for the two largest of the EU accession countries, Poland and Hungary. Using linear and non-linear dynamic factor models we find for both countries that a parsimonious specification, which combines national business cycle indicators,series reflecting trade volumes and supranational business expectations makes for the most reliable business cycle leaders. The composite leading indicators significantly Granger-cause GDP growth rates, while the estimated Markov-switching probabilities of being in a recessionarystate agree well with a priori determined cycle chronologies.

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    File URL: http://www.cesifo-group.de/portal/page/portal/DocBase_Content/WP/WP-Ifo_Working_Papers/wp-ifo-2005-2010/IfoWorkingPaper-3.pdf
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    Bibliographic Info

    Paper provided by Ifo Institute for Economic Research at the University of Munich in its series Ifo Working Paper Series with number Ifo Working Paper No. 3.

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    Date of creation: 2005
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    Handle: RePEc:ces:ifowps:_3

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    Related research

    Keywords: Business Cycles; Composite Leading Indicators; EU Enlargement; Markovswitching; Turning Points;

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    References

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    1. James H. Stock & Mark W. Watson, 1992. "A Procedure for Predicting Recessions With Leading Indicators: Econometric Issues and Recent Experience," NBER Working Papers 4014, National Bureau of Economic Research, Inc.
    2. 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.
    3. Harm Bandholz & Michael Funke, 2001. "In Search of Leading Indicators of Economic Activity in Germany," CESifo Working Paper Series 571, CESifo Group Munich.
    4. Diebold & Rudebusch, . "Measuring Business Cycle: A Modern Perspective," Home Pages _061, University of Pennsylvania.
    5. Gerhard Bry & Charlotte Boschan, 1971. "Cyclical Analysis of Time Series: Selected Procedures and Computer Programs," NBER Books, National Bureau of Economic Research, Inc, number bry_71-1, May.
    6. Artis, Michael J & Kontolemis, Zenon G & Osborn, Denise R, 1997. "Business Cycles for G7 and European Countries," The Journal of Business, University of Chicago Press, vol. 70(2), pages 249-79, April.
    7. 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.
    8. Michael Artis & Massimiliano Marcellino & Tommaso Proietti, 2004. "Characterising the Business Cycle for Accession Countries," Working Papers 261, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    9. 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.
    10. Bandholz, Harm & Funke, Michael, 2003. "Die Konstruktion und Schätzung eines Konjunkturfrühindikators für Hamburg," Wirtschaftsdienst – Zeitschrift für Wirtschaftspolitik (1998 - 2007), ZBW – German National Library of Economics / Leibniz Information Centre for Economics, vol. 83(8), pages 540-548.
    11. Victor Zarnowitz, 1963. "Cloos on Reference Dates and Leading Indicators: A Comment," The Journal of Business, University of Chicago Press, vol. 36, pages 461.
    12. Zsolt Darvas & György Szapáry, 2006. "Business Cycle Synchronization in the Enlarged EU," Working Papers 0604, Department of Mathematical Economics and Economic Analysis, Corvinus University of Budapest.
    13. Goldfeld, Stephen M. & Quandt, Richard E., 1973. "A Markov model for switching regressions," Journal of Econometrics, Elsevier, vol. 1(1), pages 3-15, March.
    14. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2004. "Forecasting Macroeconomic Variables for the Acceding Countries," Working Papers 260, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    15. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
    16. Kim, Chang-Jin, 1994. "Dynamic linear models with Markov-switching," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 1-22.
    17. Boldin, Michael D, 1994. "Dating Turning Points in the Business Cycle," The Journal of Business, University of Chicago Press, vol. 67(1), pages 97-131, January.
    18. Hamilton, James D., 1990. "Analysis of time series subject to changes in regime," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 39-70.
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    Cited by:
    1. Konstantin A. Kholodilin, 2006. "Using the Dynamic Bi-Factor Model with Markov Switching to Predict the Cyclical Turns in the Large European Economies," Discussion Papers of DIW Berlin 554, DIW Berlin, German Institute for Economic Research.
    2. Konstantin A. Kholodilin, 2005. "Forecasting the German Cyclical Turning Points: Dynamic Bi-Factor Model with Markov Switching," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 225(6), pages 653-674, November.
    3. Konstantin A. Kholodilin, 2005. "Forecasting the Turns of German Business Cycle: Dynamic Bi-factor Model with Markov Switching," Discussion Papers of DIW Berlin 494, DIW Berlin, German Institute for Economic Research.

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