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Bandwidth Selection for Semiparametric Estimators Using the m-out-of-n Bootstrap


  • Chuan Goh


This paper considers a class of semiparametric estimators that take the form of density-weighted averages. These arise naturally in a consideration of semiparametric methods for the estimation of index and sample-selection models involving preliminary kernel density estimates. The question considered in this paper is that of selecting the degree of smoothing to be used in computing the preliminary density estimate. This paper proposes a bootstrap method for estimating the mean squared error and associated optimal bandwidth. The particular bootstrap method suggested here involves using a resample of smaller size than the original sample. This method of bandwidth selection is presented with specific reference to the case of estimators of average densities, of density-weighted average derivatives and of density-weighted conditional covariances.

Suggested Citation

  • Chuan Goh, 2007. "Bandwidth Selection for Semiparametric Estimators Using the m-out-of-n Bootstrap," Working Papers tecipa-274, University of Toronto, Department of Economics.
  • Handle: RePEc:tor:tecipa:tecipa-274

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    More about this item


    bandwidth selection; density-weighted averages; bootstrap; m-out-of-n bootstrap; kernel density estimation;

    JEL classification:

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

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