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Estimation of Parameters in a Linear Regression Model under the Kullback-Leibler Loss


  • Tatsuya Kubokawa

    (Faculty of Economics, University of Tokyo)

  • Hisayuki Tsukuma

    (Faculty of Medicine, Toho University)


This paper is concerned with the simultaneous estimation of parameters of regression coefficients and error variance in a linear regression model. Motivated from the Akaike information criterion, the expected Kullback-Leibler distance is employed as a risk function for comparing estimators in a decision-theoretic framework. This setup gives us the difficulty in handling the risk because an estimator of the variance is incorporated into the loss for estimating the regression coefficients. In this situation, several estimators of the variance and the regression coefficients are proposed and shown to improve on usual estimators used as a benchmark. Through simulation studies for the risk behavior of estimators, it is numerically shown that a truncated estimator has more favorable risk than the usual estimators.

Suggested Citation

  • Tatsuya Kubokawa & Hisayuki Tsukuma, 2005. "Estimation of Parameters in a Linear Regression Model under the Kullback-Leibler Loss," CIRJE F-Series CIRJE-F-389, CIRJE, Faculty of Economics, University of Tokyo.
  • Handle: RePEc:tky:fseres:2005cf389

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    References listed on IDEAS

    1. Aoki, Masanao, 2002. "Open models of share markets with two dominant types of participants," Journal of Economic Behavior & Organization, Elsevier, vol. 49(2), pages 199-216, October.
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