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arXiv · 0909.5390

Errors-in-variables models: a generalized functions approach

Victoria Zinde-Walsh
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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. A consistent nonparametric plug-in estimator for regression functions in the space of absolutely integrable functions 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 error is not required.

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Victoria Zinde-Walsh. 2009-09-29. Errors-in-variables models: a generalized functions approach. https://arxiv.org/abs/0909.5390

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