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Improved Estimation Of The Expected Kullback–Leibler Discrepancy In Case Of Misspecification

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  • Reschenhofer, Erhard

Abstract

In case of misspecification, the Akaike information criterion (AIC; Akaike, 1973, in Petrov & Csaki, eds., Second International Symposium on Information Theory, pp. 267–281. Budapest: Akademia Kiado) is an asymptotically biased estimator of the expected Kullback–Leibler discrepancy. This paper gives simple expressions for the bias that can be used to construct improved estimators. However, for the examples that are considered in detail it turns out that model selection procedures based on such improved estimators are nearly equivalent to model selection procedures based on severely biased estimators.

Suggested Citation

  • Reschenhofer, Erhard, 1999. "Improved Estimation Of The Expected Kullback–Leibler Discrepancy In Case Of Misspecification," Econometric Theory, Cambridge University Press, vol. 15(3), pages 377-387, June.
    • Handle: RePEc:cup:etheor:v:15:y:1999:i:03:p:377-387_15
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    Cited by:

    1. Costantini, Mauro & Kunst, Robert M., 2021. "On using predictive-ability tests in the selection of time-series prediction models: A Monte Carlo evaluation," International Journal of Forecasting, Elsevier, vol. 37(2), pages 445-460.
    2. Erhard Reschenhofer & Michael Schilde & Eva Oberecker & Ellen Payr & Hasan Tandogan & Lea Wakolbinger, 2012. "Identifying the determinants of foreign direct investment: a data-specific model selection approach," Statistical Papers, Springer, vol. 53(3), pages 739-752, August.

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