文章基本信息

标题：MELE: MAXIMUM ENTROPY LEUVEN ESTIMATORS
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作者：Paris, Quirino
期刊名称：Journal of Food Distribution Research
印刷版ISSN：0047-245X
出版年度：2001
期号：SUPPL
出版社：Food Distribution Research Society
摘要：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.
关键词：multicollinearity;mean squared error;ordinary least squares;generalized maximum entropy