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Detecting multiple breaks in time series covariance structure: a non-parametric approach based on the evolutionary spectral density

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  • Ibrahim Ahamada
  • Jamel Jouini
  • Mohamed Boutahar

Abstract

This article estimates the number of breaks and their locations in the covariance structure of a series based on the evolutionary spectral density and uses some standard information criteria. The adopted approach is non-parametric and does not privilege a priori any modelling of the series. One carries out a Monte Carlo analysis and an empirical illustration using the daily return series of exchange rate euro/US dollar to support the relevance of the theory and to produce additional insights. The simulation results are globally adequate and show that the criteria having heavy penalty are more accurate in the selection of the number of breaks. The empirical results indicate that the covariance structure of the return series considerably varies between 30 March 2000 and 6 April 2001. The unconditional volatility appears non-constant over this interval.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Applied Economics.

Volume (Year): 36 (2004)
Issue (Month): 10 ()
Pages: 1095-1101

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Handle: RePEc:taf:applec:v:36:y:2004:i:10:p:1095-1101

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  1. Ahamada, Ibrahim, 2002. "Tests for covariance stationarity and white noise, with an application to Euro/US dollar exchange rate: An approach based on the evolutionary spectral density," Economics Letters, Elsevier, vol. 77(2), pages 177-186, October.
  2. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-58, May.
  3. Perron, P. & Bai, J., 1995. "Estimating and Testing Linear Models with Multiple Structural Changes," Cahiers de recherche 9552, Universite de Montreal, Departement de sciences economiques.
  4. 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.
  5. Donald W.K. Andrews & Christopher J. Monahan, 1990. "An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator," Cowles Foundation Discussion Papers 942, Cowles Foundation for Research in Economics, Yale University.
  6. Nunes, Luis C. & Newbold, Paul & Chung-Ming Kuan, 1996. "Spurious number of breaks," Economics Letters, Elsevier, vol. 50(2), pages 175-178, February.
  7. Mohamed Safouane Ben Aissa & Jamel Jouini, 2003. "Structural breaks in the US inflation process," Applied Economics Letters, Taylor & Francis Journals, vol. 10(10), pages 633-636.
  8. Pagan, Adrian R. & Schwert, G. William, 1990. "Testing for covariance stationarity in stock market data," Economics Letters, Elsevier, vol. 33(2), pages 165-170, June.
  9. Ahamada, Ibrahim & Boutahar, Mohamed, 2003. "Erratum to "Tests for covariance stationarity and white noise, with an application to Euro/US dollar exchange rate: An approach based on the evolutionary spectral density" [Economics Letters," Economics Letters, Elsevier, vol. 78(2), pages 293-293, February.
  10. Artis, Michael J & Bladen-Hovell, Robin & Nachane, Dilip M, 1992. "Instability of the Velocity of Money: A New Approach Based on the Evolutionary Spectrum," CEPR Discussion Papers 735, C.E.P.R. Discussion Papers.
  11. Mohamed Safouane Ben Aissa & Mohamed Boutahar & Jamel Jouini, 2004. "Bai and Perron's and spectral density methods for structural change detection in the US inflation process," Applied Economics Letters, Taylor & Francis Journals, vol. 11(2), pages 109-115.
  12. Thomas Mikosch & Catalin Starica, 2004. "Long range dependence effects and ARCH modelling," Econometrics 0412004, EconWPA.
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Cited by:
  1. Bill Russell, 2007. "Non-Stationary Inflation and Panel Estimates of United States Short and Long-run Phillips curves," Dundee Discussion Papers in Economics 200, Economic Studies, University of Dundee.
  2. repec:hal:journl:halshs-00476024 is not listed on IDEAS

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