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Mele: Maximum Entropy Leuven Estimators

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  • Paris, Quirino

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 (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.

Suggested Citation

  • Paris, Quirino, 2001. "Mele: Maximum Entropy Leuven Estimators," Working Papers 11991, University of California, Davis, Department of Agricultural and Resource Economics.
    • Handle: RePEc:ags:ucdavw:11991
    DOI: 10.22004/ag.econ.11991
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    References listed on IDEAS

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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 Archive 1488, Iowa State University, Department of Economics.
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    Cited by:

    1. Balcombe, Kelvin, 2006. "Cross-Entropy Estimation of Linear Cointegrated Equations," MPRA Paper 15100, University Library of Munich, Germany.
    2. Van Huylenbroeck, Guido & Lauwers, Ludwig H. & Fernagut, Bruno, 2006. "New Developments in Agricultural Policy Modelling and Consequences for Managing the Policy Analysis Systems," 2006 Annual Meeting, August 12-18, 2006, Queensland, Australia 25256, International Association of Agricultural Economists.
    3. 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.

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