Semiparametric Deconvolution with Unknown Error Variance

William Horrace and Christopher Parmeter

No 104, Center for Policy Research Working Papers from Center for Policy Research, Maxwell School, Syracuse University

Abstract: Deconvolution is a useful statistical technique for recovering an unknown density in the presence of measurement error. Typically, the method hinges on stringent assumptions about teh nature of the measurement error, more specifically, that the distribution is *entirely* known. We relax this assumption in the context of a regression error component model and develop an estimator for the unkinown density. We show semi-uniform consistency of the estimator and provide Monte Carlo evidence that demonstrates the merits of the method.

Keywords: Error component; ordinary smooth; semi-uniform consistency (search for similar items in EconPapers)
JEL-codes: C14 C21 (search for similar items in EconPapers)
Pages: 23 pages
Date: 2008-04
New Economics Papers: this item is included in nep-ecm and nep-ore
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https://surface.syr.edu/cpr/62/ (application/pdf)

Related works:
Journal Article: Semiparametric deconvolution with unknown error variance (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:max:cprwps:104

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