An Information Theoretic Approach to Estimation in the Case of Multicollinearity
AbstractWe propose a data-based extremum formulation that extends theempirical-likelihood and information-theoretic methods of estimation andinference. It is demonstrated how this method may be used in a general linearmodel context to mitigate the problem of an ill-conditioned design matrix. Adual loss criterion function, which can be biased in finite samples, producesan estimator that is consistent and asymptotically normal. Limiting chi-squaredistributions are obtained that may be used for hypothesis testing andconfidence intervals. Empirical-risk sampling experiments suggest theestimator has excellent finite-sample properties under a squared error lossmeasure. Copyright Kluwer Academic Publishers 2003
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Bibliographic InfoArticle provided by Society for Computational Economics in its journal Computational Economics.
Volume (Year): 22 (2003)
Issue (Month): 1 (August)
empirical-likelihood; semiparametric models; extended estimating equations; Kullback–Leibler information criterion; Lagrange multiplier; pseudo-likelihood ratio tests;
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers 1488, Iowa State University, Department of Economics.
- Mittelhammer R. & Judge G. & van Akkeren M. & Cardell N.S., 2002. "Coordinate Based Empirical Likelihood-Like Estimation in Ill-Conditioned Inverse Problems," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1108-1121, December.
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