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

Tatsuya Kubokawa and Muni S. Srivastava
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Tatsuya Kubokawa: Faculty of Economics, University of Tokyo
Muni S. Srivastava: Department of Statistics, University of Toronto

No CIRJE-F-525, CIRJE F-Series from CIRJE, Faculty of Economics, University of Tokyo

Abstract: 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.

Pages: 19 pages
Date: 2007-11
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:tky:fseres:2007cf525

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