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Classical vs wavelet-based filters Comparative study and application to business cycle

Listed author(s):
  • Ibrahim Ahamada

    ()

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

  • Philippe Jolivaldt

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

In this article, we compare the performance of Hodrickk-Prescott and Baxter-King filters with a method of filtering based on the multi-resolution properties of wavelets. We show that overall the three methods remain comparable if the theoretical cyclical component is defined in the usual waveband, ranging between six and thirty two quarters. However the approach based on wavelets provides information about the business cycle, for example, its stability over time which the other two filters do not provide. Based on Monte Carlo simulation experiments, our method applied to the American GDP using growth rate data shows that the estimate of the business cycle component is richer in information than that deduced from the level of GDP and includes additional information about the post 1980 period of great moderation.

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File URL: https://halshs.archives-ouvertes.fr/halshs-00476022/document
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Paper provided by HAL in its series Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) with number halshs-00476022.

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Date of creation: Mar 2010
Publication status: Published in Documents de travail du Centre d'Economie de la Sorbonne 2010.27 - ISSN : 1955-611X. 2010
Handle: RePEc:hal:cesptp:halshs-00476022
Note: View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-00476022
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  1. Marianne Baxter & Robert G. King, 1999. "Measuring Business Cycles: Approximate Band-Pass Filters For Economic Time Series," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 575-593, November.
  2. Christoph Schleicher, 2002. "An Introduction to Wavelets for Economists," Staff Working Papers 02-3, Bank of Canada.
  3. Hodrick, Robert J & Prescott, Edward C, 1997. "Postwar U.S. Business Cycles: An Empirical Investigation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 29(1), pages 1-16, February.
  4. Ibrahim Ahmada & Mohamed Safouane Ben Aissa, 2005. "Changements Structurels dans la Dynamique de l'Inflation aux Etats-Unis : Approches Non Paramétriques," Annals of Economics and Statistics, GENES, issue 77, pages 157-172.
  5. Harvey, A C & Jaeger, A, 1993. "Detrending, Stylized Facts and the Business Cycle," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(3), pages 231-247, July-Sept.
  6. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1.
  7. Ramsey, J.B., 2002. "Wavelets in Economics and Finance: Past and Future," Working Papers 02-02, C.V. Starr Center for Applied Economics, New York University.
  8. King, Robert G. & Rebelo, Sergio T., 1993. "Low frequency filtering and real business cycles," Journal of Economic Dynamics and Control, Elsevier, vol. 17(1-2), pages 207-231.
  9. Singleton, Kenneth J., 1988. "Econometric issues in the analysis of equilibrium business cycle models," Journal of Monetary Economics, Elsevier, vol. 21(2-3), pages 361-386.
  10. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
  11. Ramsey James B., 2002. "Wavelets in Economics and Finance: Past and Future," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 6(3), pages 1-29, November.
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