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Business Cycle Indexes: Does a Heap of Data Help?

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Author Info

  • 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.

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File URL: http://dx.doi.org/10.1787/jbcma-v2004-art17-en
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Bibliographic Info

Article provided by OECD Publishing,CIRET in its journal Journal of Business Cycle Measurement and Analysis.

Volume (Year): 2004 (2004)
Issue (Month): 3 ()
Pages: 309-336

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Handle: RePEc:oec:stdkaa:5lgv257l0qd2

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Related research

Keywords: Business cycles indexes; Coincident and leading indicators; NBER method; Generalized dynamic factor model; Euro area business cycle;

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References

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  1. n/a, 2002. "Credibility of the Russian Stabilisation Programme in 1995-98," NIESR Discussion Papers 149, National Institute of Economic and Social Research.
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Cited by:
  1. Maximo Camacho & Gabriel Perez-Quiros, 2008. "Introducing the EURO-STING: Short Term INdicator of Euro Area Growth," Banco de Espa�a Working Papers 0807, Banco de Espa�a.
  2. 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.
  3. Christian Gillitzer & Jonathan Kearns & Anthony Richards, 2005. "The Australian Business Cycle: A Coincident Indicator Approach," RBA Research Discussion Papers rdp2005-07, Reserve Bank of Australia.
  4. Everts, Martin, 2006. "Duration of Business Cycles," MPRA Paper 1219, University Library of Munich, Germany.
  5. 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.
  6. Jan P.A.M. Jacobs & Pieter W. Otter & Ard H.J. den Reijer, 2011. "Information, data dimension and factor structure," CAMA Working Papers 2011-15, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  7. Ard H.J. den Reijer, 2005. "Forecasting Dutch GDP using Large Scale Factor Models," DNB Working Papers 028, Netherlands Central Bank, Research Department.
  8. 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.
  9. Jean Boivin & Serena Ng, 2003. "Are More Data Always Better for Factor Analysis?," NBER Working Papers 9829, National Bureau of Economic Research, Inc.

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