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A Simple Deconvolving Kernel Density Estimator when Noise is Gaussian

Author

Listed:
  • Isabel Proenca

    (ISEG-UTL)

Abstract

Deconvolving kernel estimators when noise is Gaussian entail heavy calculations. In order to obtain the density estimates numerical evaluation of a specific integral is needed. This work proposes an approximation to the deconvolving kernel which simplifies considerably calculations by avoiding the typical numerical integration. Simulations included indicate that the lost in performance relatively to the true deconvolving kernel, is almost negligible in finite samples.

Suggested Citation

  • Isabel Proenca, 2005. "A Simple Deconvolving Kernel Density Estimator when Noise is Gaussian," Econometrics 0508006, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpem:0508006
    Note: Type of Document - pdf; prepared on windows; pages: 9. pdf for Windows document submitted via ftp
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    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/em/papers/0508/0508006.pdf
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    References listed on IDEAS

    as
    1. Laurent Calvet & Etienne Comon, 2003. "Behavioral Heterogeneity and the Income Effect," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 653-669, August.
    2. Joel L. Horowitz & Marianthi Markatou, 1996. "Semiparametric Estimation of Regression Models for Panel Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 63(1), pages 145-168.
    3. Joel L. Horowitz & Marianthi Markatou, 1993. "Semiparametric Estimation Of Regression Models For Panel Data," Econometrics 9309001, University Library of Munich, Germany.
    4. Wand, M. P., 1998. "Finite sample performance of deconvolving density estimators," Statistics & Probability Letters, Elsevier, vol. 37(2), pages 131-139, February.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    deconvolution; density estimation; errors-in-variables; kernel; simulations;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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