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An Improved Divergence Information Criterion for the Determination of the Order of an AR Process
University of Lund. (stat@oru)
University of Cyprus, Cyprus.
University of Cyprus, Cyprus.
2010 (English)In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 39, no 5, p. 865-879Article in journal (Refereed) Published
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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.

Place, publisher, year, edition, pages
Taylor & Francis , 2010. Vol. 39, no 5, p. 865-879
Keywords [en]
AR process, Information criterion, Measure of divergence, Model selection, 62M10, 62F07, 91B84, 94A15
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:lnu:diva-50573DOI: 10.1080/03610911003650391ISI: 000277568500001Scopus ID: 2-s2.0-77952391452OAI: oai:DiVA.org:lnu-50573DiVA, id: diva2:911073
Available from: 2015-01-08 Created: 2016-03-11 Last updated: 2017-11-30Bibliographically approved

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Mantalos, Panagiotis

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