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Business Cycle Indexes: Does a Heap of Data Help?

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  • Robert Inklaar

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  • Jan Jacobs
  • Ward Romp

Abstract

Business cycle indexes are used to get a timely and frequent description of the state of the economy and its likely development in the near future. This paper discusses two methods for constructing business cycle indexes, the traditional NBER method and a recently developed dynamic factor model, and compares these methods for the euro area. The results suggest that a reliable index can be constructed from a limited number of series that are selected using economic logic. We next decompose this index to identify variables that seem to be driving the euro area cycle.Business cycle indexes are used to get a timely and frequent description of the state of the economy and its likely development in the near future. This paper discusses two methods for constructing business cycle indexes, the traditional NBER method and a recently developed dynamic factor model, and compares these methods for the euro area. The results suggest that a reliable index can be constructed from a limited number of series that are selected using economic logic. We next decompose this index to identify variables that seem to be driving the euro area cycle. This analysis reveals important differences across countries in these driving variables.

Suggested Citation

  • Robert Inklaar & Jan Jacobs & Ward Romp, 2005. "Business Cycle Indexes: Does a Heap of Data Help?," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2004(3), pages 309-336.
  • Handle: RePEc:oec:stdkaa:5lgv257l0qd2
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    File URL: http://dx.doi.org/10.1787/jbcma-v2004-art17-en
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    Cited by:

    1. Harun Alp & Yusuf Soner Baskaya & Mustafa Kilinc & Canan Yuksel, 2011. "Turkiye Icin Hodrick-Prescott Filtresi Duzgunlestirme Parametresi Tahmini," CBT Research Notes in Economics 1103, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    2. Ataman Ozyildirim & Brian Schaitkin & Victor Zarnowitz, 2010. "Business cycles in the euro area defined with coincident economic indicators and predicted with leading economic indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 6-28.
    3. Harun Alp & Yusuf Soner Baskaya & Mustafa Kilinc & Canan Yuksel, 2012. "Stylized Facts for Business Cycles in Turkey," Working Papers 1202, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    4. Everts, Martin, 2006. "Duration of Business Cycles," MPRA Paper 1219, University Library of Munich, Germany.
    5. Christian Gillitzer & Jonathan Kearns & Anthony Richards, 2005. "The Australian Business Cycle: A Coincident Indicator Approach," RBA Annual Conference Volume,in: Christopher Kent & David Norman (ed.), The Changing Nature of the Business Cycle Reserve Bank of Australia.
    6. Maximo Camacho & Gabriel Perez-Quiros, 2010. "Introducing the euro-sting: Short-term indicator of euro area growth," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 663-694.
    7. Jacobs, Jan P.A.M. & Otter, Pieter W. & den Reijer, Ard H.J., 2012. "Information, data dimension and factor structure," Journal of Multivariate Analysis, Elsevier, vol. 106(C), pages 80-91.
    8. Nyberg, Henri, 2013. "Predicting bear and bull stock markets with dynamic binary time series models," Journal of Banking & Finance, Elsevier, vol. 37(9), pages 3351-3363.
    9. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    10. Levanon, Gad & Manini, Jean-Claude & Ozyildirim, Ataman & Schaitkin, Brian & Tanchua, Jennelyn, 2015. "Using financial indicators to predict turning points in the business cycle: The case of the leading economic index for the United States," International Journal of Forecasting, Elsevier, vol. 31(2), pages 426-445.

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