The Akaike Information Criterion, AIC (Akaike, 1973) , and a bias-corrected version, Aic c (Sugiura, 1978; Hurvich & Tsai, 1989) are two methods for selection of regression and autoregressive models. Both criteria may be viewed as estimators of the expected Kullback-Leibler information. The bias of AIC and AIC C is studied in the underfitting case, where none of the candidate models includes the true model (Shibata, 1980 (Shibata, , 1981 Parzen, 1978) . Both normal linear regression and

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... essive candidate models are considered. The bias of AIC C is typically smaller, often dramatically smaller, than that of AIC. A simulation study in which the true model is an infinite-order autoregression shows that, even in moderate sample sizes, AIC C provides substantially better model selections than AIC.