  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.
