Advertisement

Journal of Systems Science and Complexity

, Volume 22, Issue 3, pp 372–394 | Cite as

Singular spectrum analysis: methodology and application to economics data

  • Hossein Hassani
  • Anatoly Zhigljavsky
Article

Abstract

This paper describes the methodology of singular spectrum analysis (SSA) and demonstrate that it is a powerful method of time series analysis and forecasting, particulary for economic time series. The authors consider the application of SSA to the analysis and forecasting of the Iranian national accounts data as provided by the Central Bank of the Islamic Republic of Iran.

Key words

Economic time series forecasting Iranian national accounts SSA 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    S. D. Krane, An evaluation of real GDP forecasts: 1996–2001, Economic Perspectives, 2003, http://www.chicagofed.org/publications/economicperspectives/2003/1qeppart1.pdf.
  2. 2.
    L. Y. Cao and A. Soofi, Nonlinear deterministic forecasting of daily dollar exchange rates, International Journal of Forecasting, 1999, 15(4): 421–430.CrossRefGoogle Scholar
  3. 3.
    A. Soofi and L. Y. Cao, Nonlinear Forecasting of Noisy Financial Data (ed. by Soofi and Cao), Modeling and Forecasting Financial Data: Techniques of Nonlinear Dynamics, Kluwer Academic Publishers, Boston, 2002.Google Scholar
  4. 4.
    D. A. Hsieh, Chaos and nonlinear dynamics: Application to financial markets, Journal of Finance, 1991, 46: 1839–1877.CrossRefGoogle Scholar
  5. 5.
    J. Scheinkman and B. LeBaron, Nonlinear dynamics and stock returns, Journal of Business, 1989, 62: 311–337.CrossRefGoogle Scholar
  6. 6.
    D. S. Broomhead and G. P. King, Extracting qualitative dynamics from experimental data, Physica D, 1986, 20: 217–236.MATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    N. Golyandina, V. Nekrutkin, and A. Zhigljavsky, Analysis of Time Series Structure: SSA and Related Techniques, Chapman & Hall/CRC, New York-London, 2001.MATHGoogle Scholar
  8. 8.
    D. Danilov and A. Zhigljavsky, Principal Components of Time Series: The `Caterpillar’ Method, University of St. Petersburg, St. Petersburg (In Russian), 1997.Google Scholar
  9. 9.
    J. B. Elsner and A. A. Tsonis, Singular Spectral Analysis, A New Tool in Time Series Analysis, Plenum Press, New York and London, 1996.Google Scholar
  10. 10.
    V. G. Moskvina and A. Zhigljavsky, An algorithm based on singular spectrum analysis for change-point detection, Communication in Statistics-Simulation and Computation, 2003, 32(4): 319–352.MATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    H. Hassani, Singular spectrum analysis: Methodology and comparison, Journal of Data Science, 2007, 5(2): 239–257.Google Scholar
  12. 12.
    H. Hassani, S. Heravi, and A. Zhigljavsky, Forecasting European industrial production with singular spectrum analysis, International Journal of Forecasting, 2009, 25: 103–118.CrossRefGoogle Scholar
  13. 13.
    Th. Alexandrov and N. Golyandina, The automatic extraction of time series trend and periodical components with the help of the Caterpillar-SSA approach, Exponenta Pro. 3–4 (In Russian), 2004, 54–61.Google Scholar
  14. 14.
  15. 15.
    S. L. Marple-Jr, Digital Spectral Analysis, Prentice Hall, New Jersey, 1987.Google Scholar
  16. 16.
    M. Bouvet and H. Clergeot, Eigen and singular value decomposition technique for the solution of harmonic retrieval problems, in SVD and Signal Processing: Algorithm, Applications and Architectures (Ed. by E. F. Deprettere ), North-Holland, Amsterdam, 1988, 93–114.Google Scholar
  17. 17.
    V. K. Madisetti and E. Lloyd, The Digital Signal Processing Handbook, CRC Press, Boca Raton, 1998.Google Scholar
  18. 18.
    D. Brillinger, Time Series, Data Analysis, and Theory, Holt, Rinehart and Winston, Inc., New York, 1975.Google Scholar
  19. 19.
    T. Subba Rao, Canonical factor analysis and stationary time series models. Sankhya: The Indian Journal of Statistics, 1976, 38B: 256–271.MathSciNetGoogle Scholar
  20. 20.
    T. Subba Rao and M. M. Gaber, An Introduction to Bispectral Analysis and Bilinear Time Series Models, Speringer-Verlag, 1984.Google Scholar
  21. 21.
  22. 22.
    B. Efron and R. Tibshirani, Bootstrap methods for standard error, confidence intervals and other measures of statistical accuracy, Statist. Science, 1986, 1(1): 54–75.MathSciNetGoogle Scholar
  23. 23.
  24. 24.
    F. X. Diebold and R. S. Mariano, Comparing predictive accuracy, Journal of Business, Economics and Statistics, 1995, 13: 253–263.CrossRefGoogle Scholar

Copyright information

© Academy of Mathematics & Systems Science, Beijing, China 2009

Authors and Affiliations

  1. 1.Institute for Trade Studies and Research (ITSR)TehranIran
  2. 2.Centre for Optimisation and Its Applications, School of MathematicsCardiff UniversityCardiffUK

Personalised recommendations