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Mele: Maximum Entropy Leuven Estimators

  • Paris, Quirino
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    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 (GME) estimator of Golan, Judge and Miller depends upon subjective exogenous information that affects the estimated parameters in an unpredictable way. This paper presents novel maximum entropy estimators inspired by the theory of light that do not depend upon any additional information. Monte Carlo experiments show that they are not affected by any level of multicollinearity and dominate OLS uniformly. The Leuven estimators are consistent and asymptotically normal.

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    File URL: http://purl.umn.edu/11991
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    Paper provided by University of California, Davis, Department of Agricultural and Resource Economics in its series Working Papers with number 11991.

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    Date of creation: 2001
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    Handle: RePEc:ags:ucdavw:11991
    Contact details of provider: Phone: 530-752-1517
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    Web page: http://www.agecon.ucdavis.edu/

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    1. Caputo, Michael R. & Paris, Quirino, 2008. "Comparative statics of the generalized maximum entropy estimator of the general linear model," European Journal of Operational Research, Elsevier, vol. 185(1), pages 195-203, February.
    2. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers 1488, Iowa State University, Department of Economics.
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