Abstract
The paper proposes a forecasting-technique well suited to stationary and non-stationary economic or financial data. Two methods are used which together generalize the Box-Jenkins ARIMA-technique: Optimized-Infinite-Impulse-Response-Filters generalize difference-filters and composed-threshold (piecewise linear) models generalize linear ARMA-models.
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References
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© 1998 Springer Science+Business Media Dordrecht
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Wildi, M. (1998). Forecasting Non-Stationary Financial Data with OIIR-Filters and Composed Threshold Models. In: Refenes, AP.N., Burgess, A.N., Moody, J.E. (eds) Decision Technologies for Computational Finance. Advances in Computational Management Science, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5625-1_31
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DOI: https://doi.org/10.1007/978-1-4615-5625-1_31
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-7923-8309-3
Online ISBN: 978-1-4615-5625-1
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