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Fractional integration and business cycle features

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  • Candelon, Bertrand
  • Gil-Alaña, Luis A.

Abstract

We show in this article that fractionally integrated univariate models for GDP may lead to a better replication of business cycle characteristics. We firstly show that the business cycle features are clearly affected by the degree of integration as well as by the other short run components of the series. Then, we model the real GDP in France, the UK and the US by means of fractionally ARIMA (ARFIMA) models, and show that the three time series can be specified in terms of this type of models with orders of integration higher than one but smaller than two. Comparing the ARFIMA specifications with those based on ARIMA models, we show via simulations that the former better describes the business cycles features of the data at least for the cases of the UK and the US. --

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Bibliographic Info

Paper provided by Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes in its series SFB 373 Discussion Papers with number 2001,46.

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Date of creation: 2001
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Handle: RePEc:zbw:sfb373:200146

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Keywords: Long memory; Business cycles; Fractional integration;

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References

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  1. Diebold, Francis X & Rudebusch, Glenn D, 1992. "Have Postwar Economic Fluctuations Been Stabilized?," American Economic Review, American Economic Association, vol. 82(4), pages 993-1005, September.
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  3. Granger, C. W. J., 1981. "Some properties of time series data and their use in econometric model specification," Journal of Econometrics, Elsevier, vol. 16(1), pages 121-130, May.
  4. L. A. Gil-Alana & P. M. Robinson, 2001. "Testing of seasonal fractional integration in UK and Japanese consumption and income," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(2), pages 95-114.
  5. Pierre-Yves Hénin & Bertrand Candelon, 1995. "La récession des années quatre-vingt dix a-t-elle été exceptionnelle ?," Économie et Prévision, Programme National Persée, vol. 120(4), pages 51-71.
  6. Gil-Alana, Luis A., 2000. "Mean reversion in the real exchange rates," Economics Letters, Elsevier, vol. 69(3), pages 285-288, December.
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  10. Francis X. Diebold & Glenn D. Rudebusch, 1988. "Long memory and persistence in aggregate output," Finance and Economics Discussion Series 7, Board of Governors of the Federal Reserve System (U.S.).
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  12. Hess, Gregory D & Iwata, Shigeru, 1997. "Measuring and Comparing Business-Cycle Features," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(4), pages 432-44, October.
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Citations

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Cited by:
  1. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2004. "Long Memory At The Long Run And At The Cyclical Frequencies: Modelling Real Wages In England, 1260 -1994," Economics and Finance Discussion Papers 04-21, Economics and Finance Section, School of Social Sciences, Brunel University.
  2. Guglielmo Maria Caporale & Juncal Cuñado & Luis A. Gil-Alana, 2013. "Modelling long-run trends and cycles in financial time series data," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(3), pages 405-421, 05.
  3. Ekaterini Panopoulou & Nikitas Pittis & Sarantis Kalyvitis, 2006. "Looking far in the past: Revisiting the growth-returns nexus with non-parametric tests," The Institute for International Integration Studies Discussion Paper Series iiisdp134, IIIS.
  4. L.A. Gil-Alana & G.M. caporale, 2004. "Long-run and Cyclical Dynamics in the US Stock Market," Econometric Society 2004 Latin American Meetings 344, Econometric Society.
  5. L.A. Gil-Alanaa, 2007. "Testing The Existence of Multiple Cycles in Financial and Economic Time Series," Annals of Economics and Finance, Society for AEF, vol. 8(1), pages 1-20, May.
  6. Roger Bowden & Jennifer Zhu, 2010. "Multi-scale variation, path risk and long-term portfolio management," Quantitative Finance, Taylor & Francis Journals, vol. 10(7), pages 783-796.

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