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Akaike Information Criterion for Selecting Variables in a Nested Error Regression Model


  • Tatsuya Kubokawa

    (Faculty of Economics, University of Tokyo)

  • Muni S. Srivastava

    (Department of Statistics, University of Toronto)


The Akaike Information Criterion (AIC) is developed for selecting the variables of a nested error regression model where an unobservable random effect is present. Using the idea of decomposing the marginal distribution into two parts of 'within' and 'between' analysis of variance, we derive the AIC when the number of groups is large. The unconditional AIC, the conditional AIC and the proposed AIC are compared using simulation. Based on the rates of selecting the true model, the proposed AIC performs better.

Suggested Citation

  • Tatsuya Kubokawa & Muni S. Srivastava, 2007. "Akaike Information Criterion for Selecting Variables in a Nested Error Regression Model," CIRJE F-Series CIRJE-F-525, CIRJE, Faculty of Economics, University of Tokyo.
  • Handle: RePEc:tky:fseres:2007cf525

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    References listed on IDEAS

    1. Matsushima, Hitoshi, 2008. "Role of honesty in full implementation," Journal of Economic Theory, Elsevier, vol. 139(1), pages 353-359, March.
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    7. Martin J. Osborne & Ariel Rubinstein, 1994. "A Course in Game Theory," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262650401, January.
    8. Maskin, Eric & Sjostrom, Tomas, 2002. "Implementation theory," Handbook of Social Choice and Welfare,in: K. J. Arrow & A. K. Sen & K. Suzumura (ed.), Handbook of Social Choice and Welfare, edition 1, volume 1, chapter 5, pages 237-288 Elsevier.
    9. Abreu, Dilip & Matsushima, Hitoshi, 1992. "Virtual Implementation in Iteratively Undominated Strategies: Complete Information," Econometrica, Econometric Society, vol. 60(5), pages 993-1008, September.
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