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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
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
This paper has been announced in the following NEP Reports:
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.:
- Whitney K. Newey & Richard J. Smith, 2004.
"Higher Order Properties of Gmm and Generalized Empirical Likelihood Estimators,"
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 CWP04/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Caroline Wise).
If references are entirely missing, you can add them using this form.