Set identification of generalized linear predictors in the presence of non-classical measurement errors

This paper studies the identification of coefficients in generalized linear predictors where the outcome variable suffers from non-classical measurement errors. Combining a mixture model of data errors with the bounding procedure proposed by Stoye (2007), I derive bounds on the coefficient vector under different non-parametric assumptions about the structure of the measurement error. The method is illustrated by analyzing a simple earnings equation.

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Year of publication:	2013
Authors:	Wüthrich, Kaspar
Publisher:	Bern : University of Bern, Department of Economics
Subject:	Generalized linear predictor \| Non-classical measurement error \| Contaminated sampling \| Corrupt sampling \| Multiplicative mean independence \| Stochastic dominance \| Nonparametric bounds

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Series:	Discussion Papers ; 13-04
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Working Paper
Language:	English
Other identifiers:	766855147 [GVK] hdl:10419/103845 [Handle] RePEc:ube:dpvwib:dp1304 [RePEc]
Classification:	C2 - Econometric Methods: Single Equation Models ; C21 - Cross-Sectional Models; Spatial Models ; J24 - Human Capital; Skills; Occupational Choice; Labor Productivity
Source:	EconStor

Persistent link: https://www.econbiz.de/10010427104