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Long cycles in growth : explorations using new frequency domain techniques with US data

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  • Crowley, Patrick M.

Abstract

In his celebrated 1966 Econometrica article, Granger first hypothesized that there is a typical spectral shape for an economic variable. This typical shape implies decreasing levels of energy as frequency increases, which in turn implies an extremely long cycle in economic fluctuations and particulary in growth. Spectral analysis is however based on certain assumptions particulary in that render these basic frequency domain techniques inappropriate for analysing non-stationary economic data. In this paper three recent frequency domain methods for extracting cycles from non-stationary data are used with US real GNP data to analyse fluctuations in economic growth. The findings, among others, are that these more recent frequency domain techniques do not provide evidence to support the typical spectral shape and nor an extremely long growth cycle á la Granger.

Suggested Citation

  • Crowley, Patrick M., 2010. "Long cycles in growth : explorations using new frequency domain techniques with US data," Research Discussion Papers 6/2010, Bank of Finland.
  • Handle: RePEc:bof:bofrdp:2010_006
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    References listed on IDEAS

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    12. Andrew Hughes Hallett & Christian R. Richter, 2004. "Spectral Analysis as a Tool for Financial Policy: An Analysis of the Short-End of the British Term Structure," Computational Economics, Springer;Society for Computational Economics, vol. 23(3), pages 271-288, April.
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    Cited by:

    1. Tausch, Arno, 2013. "The hallmarks of crisis. A new center-periphery perspective on long cycles," MPRA Paper 48356, University Library of Munich, Germany.
    2. Crowley, Patrick M. & Hughes Hallett, Andrew, 2015. "Great moderation or “Will o’ the Wisp”? A time–frequency decomposition of GDP for the US and UK," Journal of Macroeconomics, Elsevier, vol. 44(C), pages 82-97.
    3. Gallegati, Marco & Ramsey, James B., 2013. "Structural change and phase variation: A re-examination of the q-model using wavelet exploratory analysis," Structural Change and Economic Dynamics, Elsevier, vol. 25(C), pages 60-73.
    4. Benhmad, François, 2012. "Modeling nonlinear Granger causality between the oil price and U.S. dollar: A wavelet based approach," Economic Modelling, Elsevier, vol. 29(4), pages 1505-1514.
    5. Jia, Xiaoliang & An, Haizhong & Fang, Wei & Sun, Xiaoqi & Huang, Xuan, 2015. "How do correlations of crude oil prices co-move? A grey correlation-based wavelet perspective," Energy Economics, Elsevier, vol. 49(C), pages 588-598.
    6. Crowley, Patrick M. & Hudgins, David, 2015. "Euro area monetary and fiscal policy tracking design in the time-frequency domain," Research Discussion Papers 12/2015, Bank of Finland.
    7. Benhmad, François, 2013. "Dynamic cyclical comovements between oil prices and US GDP: A wavelet perspective," Energy Policy, Elsevier, vol. 57(C), pages 141-151.
    8. Bai, Limiao & Yan, Sen & Zheng, Xiaolian & Chen, Ben M., 2015. "Market turning points forecasting using wavelet analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 184-197.
    9. repec:bof:bofrdp:urn:nbn:fi:bof-201508131350 is not listed on IDEAS
    10. Jia, Xiaoliang & An, Haizhong & Sun, Xiaoqi & Huang, Xuan & Wang, Lijun, 2017. "Evolution of world crude oil market integration and diversification: A wavelet-based complex network perspective," Applied Energy, Elsevier, vol. 185(P2), pages 1788-1798.
    11. Luís Aguiar-Conraria & Maria Joana Soares, 2014. "The Continuous Wavelet Transform: Moving Beyond Uni- And Bivariate Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 28(2), pages 344-375, April.

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