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Forecasting ARMA models: a comparative study of information criteria focusing on MDIC
Lund University. (stat@oru)
University of Cyprus, Cyprus.
University of Cyprus, Cyprus.
2010 (English)In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 80, no 1, 61-73 p.Article in journal (Refereed) Published
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Abstract [en]

This paper deals with the implementation of model selection criteria to data generated by ARMA processes. The recently introduced modified divergence information criterion is used and compared with traditional selection criteria like the Akaike information criterion (AIC) and the Schwarz information criterion (SIC). The appropriateness of the selected model is tested for one- and five-step ahead predictions with the use of the normalized mean squared forecast errors (NMSFE).

Place, publisher, year, edition, pages
2010. Vol. 80, no 1, 61-73 p.
Keyword [en]
ARMA process, information criterion, model selection, MDIC, NMSFE
National Category
Probability Theory and Statistics
Research subject
Statistics/Econometrics
Identifiers
URN: urn:nbn:se:lnu:diva-50574DOI: 10.1080/00949650802464137ISI: 000273913800005Scopus ID: 2-s2.0-77649139313OAI: oai:DiVA.org:lnu-50574DiVA: diva2:911074
Available from: 2015-01-08 Created: 2016-03-11 Last updated: 2016-05-03Bibliographically approved

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Mantalos, Panagiotis
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