Generalised empirical likelihood-based kernel density estimation
AbstractIf 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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Bibliographic InfoPaper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 662.
Date of creation: 02 Jul 2013
Date of revision:
Weighted kernel density estimation; moment conditions; higher-order expansions; normal mixtures;
Other versions of this item:
- Vitaliy Oryshchenko & Richard J. Smith, 2013. "Generalised empirical likelihood-based kernel density estimation," Economics Papers 2013-W03, Economics Group, Nuffield College, University of Oxford.
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
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- Whitney K. Newey & Richard J. Smith, 2004.
"Higher Order Properties of Gmm and Generalized Empirical Likelihood Estimators,"
Econometrica, Econometric Society,
Econometric Society, vol. 72(1), pages 219-255, 01.
- Whitney Newey & Richard Smith, 2003. "Higher order properties of GMM and generalised empirical likelihood estimators," CeMMAP working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies CWP04/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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