A Simple Deconvolving Kernel Density Estimator when Noise is Gaussian

Isabel Proença ()

Econometrics from University Library of Munich, Germany

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.

Keywords: deconvolution; density estimation; errors-in-variables; kernel; simulations (search for similar items in EconPapers)
JEL-codes: C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Pages: 9 pages
Date: 2005-08-05
New Economics Papers: this item is included in nep-ets
Note: Type of Document - pdf; prepared on windows; pages: 9. pdf for Windows document submitted via ftp
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://econwpa.ub.uni-muenchen.de/econ-wp/em/papers/0508/0508006.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wpa:wuwpem:0508006

Access Statistics for this paper

More papers in Econometrics from University Library of Munich, Germany
Bibliographic data for series maintained by EconWPA ( this e-mail address is bad, please contact ).