Errors-in-Variables Models: A Generalized Functions Approach

Victoria Zinde-Walsh

Cahiers de recherche from Centre interuniversitaire de recherche en économie quantitative, CIREQ

Abstract: Generalized functions are a powerful tool for examining errors-in-variables models, since they extend consideration to wide modelclasses. Schennach (Econometrica, 2007) - (S) applies this approach to prove identification in a general class of models. Here the problems addressed in (S) are revisited because various features of the generalized functions approach need to be clari?ed. The nonparametric identification theorem in (S) applies less generally than claimed (e.g. disallowing functions with fractional power growth) by relying on decomposition of generalized functions into ordinary and singular parts which may not hold. This paper highlights the issues of importance in applying generalized functions and provides the general nonparametric identification result relating it to possibility of estimation.

Keywords: errors-in-variables model; generalized functions (search for similar items in EconPapers)
Pages: 38 pages
Date: 2007
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
Working Paper: ERRORS-IN-VARIABLES MODELS: A GENERALIZED FUNCTIONS APPROACH (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:mtl:montec:14-2007

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