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ERRORS-IN-VARIABLES MODELS: A GENERALIZED FUNCTIONS APPROACH

Victoria Zinde-Walsh

Departmental Working Papers from McGill University, Department of Economics

Abstract: Identification in errors-in-variables regression models was recently extended to wide models classes by S. Schennach (Econometrica, 2007) (S) via use of generalized functions. In this paper the problems of non- and semi- parametric identification in such models are re-examined. Nonparametric identification holds under weaker assumptions than in (S); the proof here does not rely on decomposition of generalized functions into ordinary and singular parts, which may not hold. Conditions for continuity of the identification mapping are provided and a consistent nonparametric plug-in estimator for regression functions in the L₁ space constructed. Semiparametric identification via a finite set of moments is shown to hold for classes of functions that are explicitly characterized; unlike (S) existence of a moment generating function for the measurement

JEL-codes: C14 C65 (search for similar items in EconPapers)
Pages: 37 pages
Date: 2009-09
New Economics Papers: this item is included in nep-ecm
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Working Paper: Errors-in-Variables Models: A Generalized Functions Approach (2007) Downloads
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