A Simple Deconvolving Kernel Density Estimator when Noise is Gaussian
AbstractDeconvolving 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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Bibliographic InfoPaper provided by EconWPA in its series Econometrics with number 0508006.
Length: 9 pages
Date of creation: 05 Aug 2005
Date of revision:
Note: Type of Document - pdf; prepared on windows; pages: 9. pdf for Windows document submitted via ftp
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deconvolution; density estimation; errors-in-variables; kernel; simulations;
Find related papers by JEL classification:
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
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- 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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- Wand, M. P., 1998. "Finite sample performance of deconvolving density estimators," Statistics & Probability Letters, Elsevier, vol. 37(2), pages 131-139, February.
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- Joel L. Horowitz & Marianthi Markatou, 1993. "Semiparametric Estimation Of Regression Models For Panel Data," Econometrics 9309001, EconWPA.
- 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.
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