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An Improved Divergence Information Criterion for the Determination of the Order of an AR Process
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: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 39, nr 5, s. 865-879Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

In this article we propose a modification of the recently introduced divergence information criterion (DIC, Mattheou et al., 2009) for the determination of the order of an autoregressive process and show that it is an asymptotically unbiased estimator of the expected overall discrepancy, a nonnegative quantity that measures the distance between the true unknown model and a fitted approximating model. Further, we use Monte Carlo methods and various data generating processes for small, medium, and large sample sizes in order to explore the capabilities of the new criterion in selecting the optimal order in autoregressive processes and in general in a time series context. The new criterion shows remarkably good results by choosing the correct model more frequently than traditional information criteria.

sted, utgiver, år, opplag, sider
Taylor & Francis, 2010. Vol. 39, nr 5, s. 865-879
Emneord [en]
AR process, Information criterion, Measure of divergence, Model selection, 62M10, 62F07, 91B84, 94A15
HSV kategori
Forskningsprogram
Statistik
Identifikatorer
URN: urn:nbn:se:oru:diva-40400DOI: 10.1080/03610911003650391ISI: 000277568500001Scopus ID: 2-s2.0-77952391452OAI: oai:DiVA.org:oru-40400DiVA, id: diva2:777237
Tilgjengelig fra: 2015-01-08 Laget: 2015-01-08 Sist oppdatert: 2017-12-05bibliografisk kontrollert

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