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The model selection criterion AICu

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  • McQuarrie, Allan
  • Shumway, Robert
  • Tsai, Chih-Ling
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    Abstract

    For regression and time series model selection, Hurvich and Tsai (1989) obtained a bias correction Akaike information criterion, AICc, which provides better model order choices than the Akaike information criterion, AIC (Akaike, 1973). In this paper, we propose an alternative improved regression model selection criterion, AICu, which is an approximate unbiased estimator of Kullback-Leibler information. We show that AICu is neither a consistent (Shibata, 1986) nor an efficient (Shibata, 1980, 1981) criterion. Our simulation studies indicate that the behavior of AICu is a compromise between that of efficient (AICc) and consistent (BIC, Akaike, 1978) criteria. Specifically, AICu performs better than AICc for moderate to large sample sizes except when the true model is of infinite order. In addition, it outperforms BIC except when a true model exists and the sample size is large.

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    Bibliographic Info

    Article provided by Elsevier in its journal Statistics & Probability Letters.

    Volume (Year): 34 (1997)
    Issue (Month): 3 (June)
    Pages: 285-292

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    Handle: RePEc:eee:stapro:v:34:y:1997:i:3:p:285-292

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    Related research

    Keywords: AIC AICu BIC Kullback-Leibler information;

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    Cited by:
    1. De Gooijer, Jan G. & Vidiella-i-Anguera, Antoni, 2003. "Nonlinear stochastic inflation modelling using SEASETARs," Insurance: Mathematics and Economics, Elsevier, vol. 32(1), pages 3-18, February.
    2. Jan G. de Gooijer & Antoni Vidiella-i-Anguera, 2000. "Modelling Seasonalities in Nonlinear Inflation Rates using SEASETARs," Tinbergen Institute Discussion Papers 00-098/4, Tinbergen Institute.
    3. Sýdýka Baþçý & Asad Zaman, 1998. "Variance Estimates and Model Selection," Departmental Working Papers 9814, Bilkent University, Department of Economics.
    4. Hacker, Scott, 2010. "The Effectiveness of Information Criteria in Determining Unit Root and Trend Status," Working Paper Series in Economics and Institutions of Innovation 213, Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies.
    5. Pedro Galeano & Daniel Peña, 2004. "Model Selection Criteria And Quadratic Discrimination In Arma And Setar Time Series Models," Statistics and Econometrics Working Papers ws041406, Universidad Carlos III, Departamento de Estadística y Econometría.
    6. McQuarrie, Allan & Tsai, Chih-Ling, 1999. "Model selection in orthogonal regression," Statistics & Probability Letters, Elsevier, vol. 45(4), pages 341-349, December.

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