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Generalised empirical likelihood-based kernel density estimation

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  • Vitaliy Oryshchenko
  • Richard J. Smith

Abstract

If additional information about the distribution of a random variable is available in the form of moment conditions, a weighted kernel density estimate reflecting the extra information can be constructed by replacing the uniform weights with the generalised empirical likelihood probabilities.� It is shown that the resultant density estimator provides an improved approximation to the moment constraints.� Moreover, a reduction in variance is achieved due to the systematic use of the extra moment information.

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File URL: http://www.economics.ox.ac.uk/materials/papers/12745/paper662.pdf
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Bibliographic Info

Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 662.

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Date of creation: 02 Jul 2013
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Handle: RePEc:oxf:wpaper:662

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Keywords: Weighted kernel density estimation; moment conditions; higher-order expansions; normal mixtures;

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  1. Whitney K. Newey & Richard J. Smith, 2004. "Higher Order Properties of Gmm and Generalized Empirical Likelihood Estimators," Econometrica, Econometric Society, vol. 72(1), pages 219-255, 01.
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