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A Simple Deconvolving Kernel Density Estimator when Noise is Gaussian

  • Isabel Proenca

    (ISEG-UTL)

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.

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File URL: http://econwpa.repec.org/eps/em/papers/0508/0508006.pdf
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Paper provided by EconWPA in its series Econometrics with number 0508006.

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Length: 9 pages
Date of creation: 05 Aug 2005
Date of revision:
Handle: RePEc:wpa:wuwpem:0508006
Note: Type of Document - pdf; prepared on windows; pages: 9. pdf for Windows document submitted via ftp
Contact details of provider: Web page: http://econwpa.repec.org

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  1. Joel L. Horowitz & Marianthi Markatou, 1993. "Semiparametric Estimation Of Regression Models For Panel Data," Econometrics 9309001, EconWPA.
  2. Laurent E. Calvet & Etienne Comon, 2000. "Behavioral Heterogeneity and The Income Effect," Harvard Institute of Economic Research Working Papers 1892, Harvard - Institute of Economic Research.
  3. Wand, M. P., 1998. "Finite sample performance of deconvolving density estimators," Statistics & Probability Letters, Elsevier, vol. 37(2), pages 131-139, February.
  4. Horowitz, Joel L & Markatou, Marianthi, 1996. "Semiparametric Estimation of Regression Models for Panel Data," Review of Economic Studies, Wiley Blackwell, vol. 63(1), pages 145-68, January.
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