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Identification of the order of a fractionally differenced ARMA model
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Information Science.
1999 (English)In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 14, no 2, 161-169 p.Article in journal (Refereed) Published
Abstract [en]

Long term dependence in time series can be modelled by fractionally integrated ARMA (ARFIMA) models. For an ARFIMA process it is however impossible to identify the order of the short memory polynomials by inspection of the autocorrelation and partial autocorrelation functions. Instead information criteria such as AIC, BIC and HQIC are used to identify the order. This paper investigates the performance of the three information criteria when identifying the order in an ARFIMA model. The impression is that BIC outperforms AIC and HQIC, at least for the ARFIMA models used in this simulation. The overall performance of the information criteria, however, is poor for mixtures of AR and MA processes. Introducing long memory increases the likelihood of identifying the correct orders.

Place, publisher, year, edition, pages
1999. Vol. 14, no 2, 161-169 p.
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-42769ISI: 000081487000001OAI: oai:DiVA.org:uu-42769DiVA: diva2:70671
Available from: 2008-10-17 Created: 2008-10-17 Last updated: 2017-12-05Bibliographically approved

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Lyhagen, Johan

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