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Measuring business cycles: A wavelet analysis of economic time series

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  • Yogo, Motohiro

Abstract

Multiresolution wavelet analysis is a natural way to decompose an economic time series into trend, cycle, and noise. The method is illustrated with GDP data. The business-cycle component of the wavelet-filtered series closely resembles the series filtered by the approximate bandpass filter.

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Bibliographic Info

Article provided by Elsevier in its journal Economics Letters.

Volume (Year): 100 (2008)
Issue (Month): 2 (August)
Pages: 208-212

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Handle: RePEc:eee:ecolet:v:100:y:2008:i:2:p:208-212

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Web page: http://www.elsevier.com/locate/ecolet

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References

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  1. James H. Stock & Mark W. Watson, 2003. "Has the Business Cycle Changed and Why?," NBER Chapters, in: NBER Macroeconomics Annual 2002, Volume 17, pages 159-230 National Bureau of Economic Research, Inc.
  2. Margaret M. McConnell & Gabriel Perez Quiros, 1998. "Output fluctuations in the United States: what has changed since the early 1980s?," Staff Reports 41, Federal Reserve Bank of New York.
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Citations

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Cited by:
  1. António Rua, 2011. "A wavelet approach for factor‐augmented forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(7), pages 666-678, November.
  2. Joanna Bruzda, 2011. "Business cycle synchronization according to wavelets – the case of Poland and the euro zone member countries," Bank i Kredyt, National Bank of Poland, Economic Institute, vol. 42(3), pages 5-32.
  3. de Carvalho, Miguel & Rodrigues, Paulo C. & Rua, António, 2012. "Tracking the US business cycle with a singular spectrum analysis," Economics Letters, Elsevier, vol. 114(1), pages 32-35.
  4. Gazi Salah Uddin & Aviral Kumar Tiwari, 2013. "Measuring co-movement of oil price and exchange rate differential in Bangladesh," Economics Bulletin, AccessEcon, vol. 33(3), pages 1922-1930.
  5. Reboredo, Juan C. & Rivera-Castro, Miguel A., 2014. "Wavelet-based evidence of the impact of oil prices on stock returns," International Review of Economics & Finance, Elsevier, vol. 29(C), pages 145-176.
  6. Cifter, Atilla & Yilmazer, Sait & Cifter, Elif, 2009. "Analysis of sectoral credit default cycle dependency with wavelet networks: Evidence from Turkey," Economic Modelling, Elsevier, vol. 26(6), pages 1382-1388, November.
  7. Rua, António, 2010. "Measuring comovement in the time-frequency space," Journal of Macroeconomics, Elsevier, vol. 32(2), pages 685-691, June.
  8. Christophe Boucher & Bertrand Maillet, 2011. "Une analyse temps-fréquences des cycles financiers," Documents de travail du Centre d'Economie de la Sorbonne 11003, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
  9. Antonis A Michis, 2011. "Denoised least squars forecasting of GDP changes using indexes of consumer and business sentiment," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Proceedings of the IFC Conference on "Initiatives to address data gaps revealed by the financial crisis", Basel, 25-26 August 2010, volume 34, pages 383-392 Bank for International Settlements.
  10. Rua, António & Nunes, Luis C., 2012. "A wavelet-based assessment of market risk: The emerging markets case," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(1), pages 84-92.
  11. Julien Chevallier, 2011. "Wavelet packet transforms analysis applied to carbon prices," Economics Bulletin, AccessEcon, vol. 31(2), pages 1731-1747.
  12. Benhmad, François, 2013. "Dynamic cyclical comovements between oil prices and US GDP: A wavelet perspective," Energy Policy, Elsevier, vol. 57(C), pages 141-151.
  13. Caraiani, Petre, 2012. "Money and output: New evidence based on wavelet coherence," Economics Letters, Elsevier, vol. 116(3), pages 547-550.

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