Errors-in-variables models: a generalized functions approach
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.
Keywords
Cite
@article{arxiv.0909.5390,
title = {Errors-in-variables models: a generalized functions approach},
author = {Victoria Zinde-Walsh},
journal= {arXiv preprint arXiv:0909.5390},
year = {2009}
}