Forecasting with k-factor Gegenbauer Processes: Theory and Applications

Laurent Ferrara and Dominique Guegan ()
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Dominique Guegan: Département Mathématiques Mécanique et Informatique - URCA - Université de Reims Champagne-Ardenne

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Abstract: This paper deals with the k-factor extension of the long memory Gegenbauer process proposed by Gray et al. (1989). We give the analytic expression of the prediction function derived from this long memory process and provide the h-step-ahead prediction error when parameters are either known or estimated. We investigate the predictive ability of the k-factor Gegenbauer model on real data of urban transport traffic in the Paris area, in comparison with other short- and long-memory models.

Keywords: long memory; k-factor Gegenbauer process; prediction function; prediction error; urban transport traffic (search for similar items in EconPapers)
Date: 2001-12
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Citations: View citations in EconPapers (50)

Published in Journal of Forecasting, 2001, 20 (8), pp.581 - 601. ⟨10.1002/for.815⟩

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Journal Article: Forecasting with k-Factor Gegenbauer Processes: Theory and Applications (2001)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:halshs-00193667

DOI: 10.1002/for.815

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