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Generalised empirical likelihood-based kernel density estimation

Author

Listed:
  • 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.

Suggested Citation

  • Vitaliy Oryshchenko & Richard J. Smith, 2013. "Generalised empirical likelihood-based kernel density estimation," Economics Series Working Papers 662, University of Oxford, Department of Economics.
    • Handle: RePEc:oxf:wpaper:662
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    References listed on IDEAS

    as
    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, January.
    2. Guggenberger, Patrik & Smith, Richard J., 2005. "Generalized Empirical Likelihood Estimators And Tests Under Partial, Weak, And Strong Identification," Econometric Theory, Cambridge University Press, vol. 21(4), pages 667-709, August.
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    More about this item

    Keywords

    Weighted kernel density estimation; moment conditions; higher-order expansions; normal mixtures;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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