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Multicollinearity and maximum entropy estimators

Author

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  • Quirino Paris

    () (University of California, Davis)

Abstract

Multicollinearity hampers empirical econometrics. The remedies proposed to date suffer from pitfalls of their own. The ridge estimator is not generally accepted as a vital alternative to the ordinary least-squares (OLS) estimator because it depends upon unknown parameters. The generalized maximum entropy estimator depends upon subjective exogenous information. This paper presents a novel maximum entropy estimator that does not depend upon any additional information. Monte Carlo experiments show that it is not affected by any level of multicollinearity and dominates the OLS estimator uniformely. The same experiments provide evidence that it is asymptotically unbiased and its estimates are normally distributed.

Suggested Citation

  • Quirino Paris, 2001. "Multicollinearity and maximum entropy estimators," Economics Bulletin, AccessEcon, vol. 3(11), pages 1-9.
  • Handle: RePEc:ebl:ecbull:eb-01c20002
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    File URL: http://www.accessecon.com/pubs/EB/2001/Volume3/EB-01C20002A.pdf
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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.
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    Cited by:

    1. Mishra, SK, 2004. "Estimation under Multicollinearity: Application of Restricted Liu and Maximum Entropy Estimators to the Portland Cement Dataset," MPRA Paper 1809, University Library of Munich, Germany.
    2. Sudhanshu Mishra, 2004. "Multicollinearity and maximum entropy leuven estimator," Economics Bulletin, AccessEcon, vol. 3(25), pages 1-11.
    3. Akpalu, Wisdom & Hassan, Rashid M. & Ringler, Claudia, 2008. "Climate variability and maize yield in South Africa: Results from GME and MELE methods," IFPRI discussion papers 843, International Food Policy Research Institute (IFPRI).
    4. repec:ebl:ecbull:v:3:y:2004:i:25:p:1-11 is not listed on IDEAS

    More about this item

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables

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