2009/09/29 by Victoria Zinde‐Walsh, Zinde-Walsh, Victoria
Computer Science · Decision Sciences · Mathematics · #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Methodology (stat.ME) #Simulation Techniques and Applications #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.0909.5390
openalex publication_date 2009/09/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.