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An Information Theoretic Approach to Estimation in the Case of Multicollinearity

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  • Marco van Akkeren

Abstract

We propose a data-based extremum formulation that extends theempirical-likelihood and information-theoretic methods of estimation andinference. It is demonstrated how this method may be used in a general linearmodel context to mitigate the problem of an ill-conditioned design matrix. Adual loss criterion function, which can be biased in finite samples, producesan estimator that is consistent and asymptotically normal. Limiting chi-squaredistributions are obtained that may be used for hypothesis testing andconfidence intervals. Empirical-risk sampling experiments suggest theestimator has excellent finite-sample properties under a squared error lossmeasure. Copyright Kluwer Academic Publishers 2003

Suggested Citation

  • Marco van Akkeren, 2003. "An Information Theoretic Approach to Estimation in the Case of Multicollinearity," Computational Economics, Springer;Society for Computational Economics, vol. 22(1), pages 1-22, August.
    • Handle: RePEc:kap:compec:v:22:y:2003:i:1:p:1-22
    DOI: 10.1023/A:1024571428545
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    References listed on IDEAS

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    1. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers Archive 1488, Iowa State University, Department of Economics.
    2. Mittelhammer R. & Judge G. & van Akkeren M. & Cardell N.S., 2002. "Coordinate Based Empirical Likelihood-Like Estimation in Ill-Conditioned Inverse Problems," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1108-1121, December.
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