Consistent Density Deconvolution under Partially Known Error Distribution

We estimate the distribution of a real-valued random variable from contaminated observations. The additive error is supposed to be normally distributed, but with unknown variance. The distribution is identifiable from the observations if we restrict the class of considered distributions by a simple condition in the time domain. A minimum distance estimator is shown to be consistent imposing only a slightly stronger assumption than the identification condition.

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Year of publication:	2009-10-06
Authors:	Schwarz, Maik ; Van Bellegem, Sébastien
Institutions:	Institut d'Économie Industrielle (IDEI), Toulouse School of Economics (TSE)
Subject:	deconvolution \| error measurement \| density estimation

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Extent:	application/pdf
Series:	IDEI Working Papers.
Type of publication:	Book / Working Paper
Language:	English
Notes:	The text is part of a series IDEI Working Paper Number 632
Source:	RePEc - Research Papers in Economics

Persistent link: https://ebvufind01.dmz1.zbw.eu/10008492575