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ADVANCEMENT OF FRACTIONALLY DIFFERENCED GEGENBAUER PROCESSES WITH LONG MEMORY

Part of: Inference from stochastic processes Stochastic analysis

Published online by Cambridge University Press:  12 May 2016

GNANADARSHA SANJAYA DISSANAYAKE
GNANADARSHA SANJAYA DISSANAYAKE*
Affiliation:
School of Mathematics and Statistics, University of Sydney, New South Wales 2006, Australia email gnanadarshad@gmail.com
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Abstract

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Keywords

long memory time seriesGegenbauer processesMonte Carlo experiment

MSC classification

Primary: 62M10: Time series, auto-correlation, regression, etc.
Secondary: 60H40: White noise theory
Type
Abstracts of Australasian PhD Theses
Information
Bulletin of the Australian Mathematical Society , Volume 94 , Issue 1 , August 2016 , pp. 173 - 174
Copyright
© 2016 Australian Mathematical Publishing Association Inc. 

References

Dissanayake, G. S. and Peiris, M. S., ‘Generalized fractional processes with conditional heteroskedasticity’, Sri Lankan J. Appl. Statist. 12 (2011), 1–12.CrossRefGoogle Scholar
Dissanayake, G. S., Peiris, M. S. and Proietti, T., ‘State space modeling of Gegenbauer processes with long memory’, Comput. Statist. Data Anal., Special issue of Ann. Comput. Financ. Econom., to appear, doi:10.1016/j.csda.2014.09.014.CrossRefGoogle Scholar
Dissanayake, G. S., Peiris, M. S. and Proietti, T., ‘Estimation of generalized fractionally differenced processes with conditionally heteroskedastic errors’, in: International Work Conference on Time Series, Proc. ITISE 2014 (eds. Ruiz, I. R. and Garcia, G. R.) 871–890. Copicentro Granada S L, ISBN 978-84-15814-97-9.Google Scholar