A systems approach to recursive economic forecasting and seasonal adjustment
AbstractThe 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.
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Bibliographic InfoPaper provided by Federal Reserve Bank of Minneapolis in its series Discussion Paper / Institute for Empirical Macroeconomics with number 8.
Date of creation: 1989
Date of revision:
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- 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.
- 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.
- Stephen Pollock, 2005.
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530, Queen Mary, University of London, School of Economics and Finance.
- Ramaprasad Bhar, 2010. "Stochastic Filtering With Applications In Finance:," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 7736, October.
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