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A systems approach to recursive economic forecasting and seasonal adjustment

Author

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  • Peter Armitage
  • Cho Ng
  • Peter C. Young

Abstract

The paper discusses a new, fully recursive approach to the adaptive modeling, forecasting and seasonal adjustment of nonstationary economic time-series. The procedure is based around a time variable parameter (TVP) version of the well known component or structural model. It employs a novel method of sequential spectral decomposition (SSD), based on recursive state-space smoothing, to decompose the series into a number of quasi-orthogonal components. This SSD procedure can be considered as a complete approach to the problem of model identification and estimation, or it can be used as a first step in maximum likelihood estimation. Finally, the paper illustrates the overall adaptive approach by considering a practical example of a UK unemployment series which exhibits marked nonstationarity caused by various economic factors.

Suggested Citation

  • Peter Armitage & Cho Ng & Peter C. Young, 1989. "A systems approach to recursive economic forecasting and seasonal adjustment," Discussion Paper / Institute for Empirical Macroeconomics 8, Federal Reserve Bank of Minneapolis.
    • Handle: RePEc:fip:fedmem:8
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    References listed on IDEAS

    as
    1. Nerlove, Marc & Grether, David M. & Carvalho, José L., 1979. "Analysis of Economic Time Series," Elsevier Monographs, Elsevier, edition 1, number 9780125157506 edited by Shell, Karl.
    2. Bell, William R & Hillmer, Steven C, 1984. "Issues Involved with the Seasonal Adjustment of Time Series: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 343-349, October.
    3. Bell, William R & Hillmer, Steven C, 1984. "Issues Involved with the Seasonal Adjustment of Economic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 291-320, October.
    4. P. J. Harrison, 1967. "Exponential Smoothing and Short-Term Sales Forecasting," Management Science, INFORMS, vol. 13(11), pages 821-842, July.
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    Cited by:

    1. Ramaprasad Bhar, 2010. "Stochastic Filtering with Applications in Finance," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 7736.
    2. Young, Peter & Pedregal, Diego, 1997. "Comments on "An analysis of the international tourism demand in Spain" by P. Gonzalez and P. Moral," International Journal of Forecasting, Elsevier, vol. 13(4), pages 551-556, December.
    3. Young, Peter C. & Pedregal, Diego J., 1999. "Macro-economic relativity: government spending, private investment and unemployment in the USA 1948-1998," Structural Change and Economic Dynamics, Elsevier, vol. 10(3-4), pages 359-380, December.
    4. Pollock, D.S.G., 2006. "Econometric methods of signal extraction," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2268-2292, May.
    5. Peter Young, 1999. "Recursive and en-bloc approaches to signal extraction," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(1), pages 103-128.

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