MELE: MAXIMUM ENTROPY LEUVEN ESTIMATORS

Quirino Paris

No 11991, Working Papers from University of California, Davis, Department of Agricultural and Resource Economics

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.

Keywords: Research; Methods/Statistical; Methods (search for similar items in EconPapers)
Pages: 34
Date: 2001
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:ags:ucdavw:11991

DOI: 10.22004/ag.econ.11991

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