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Forecasting ARMA models: a comparative study of information criteria focusing on MDIC
Department of Statistics, University of Lund, Lund, Sweden. (stat@oru)
Department of Mathematics and Statistics, University of Cyprus, Nicosia, Cyprus.
Department of Mathematics and Statistics, University of Cyprus, Nicosia, Cyprus.
2010 (engelsk)Inngår i: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 80, nr 1, s. 61-73Artikkel i tidsskrift (Fagfellevurdert) Published
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).

sted, utgiver, år, opplag, sider
Taylor & Francis, 2010. Vol. 80, nr 1, s. 61-73
Emneord [en]
ARMA process, information criterion, model selection, MDIC, NMSFE
HSV kategori
Identifikatorer
URN: urn:nbn:se:oru:diva-40402DOI: 10.1080/00949650802464137ISI: 000273913800005Scopus ID: 2-s2.0-77649139313OAI: oai:DiVA.org:oru-40402DiVA, id: diva2:777228
Tilgjengelig fra: 2015-01-08 Laget: 2015-01-08 Sist oppdatert: 2018-04-24bibliografisk kontrollert

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