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) 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:	2013-08
Authors:	Wüthrich, Kaspar
Institutions:	Department Volkswirtschaftlehre, Universität Bern
Subject:	Generalized linear predictor \| Non-classical measurement error \| Contaminated sampling \| Corrupt sampling \| Multiplicative mean independence \| Stochastic dominance \| Nonparametric bounds

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Extent:	application/pdf
Series:	Diskussionsschriften.
Type of publication:	Book / Working Paper
Classification:	C2 - Econometric Methods: Single Equation Models ; C21 - Cross-Sectional Models; Spatial Models ; J24 - Human Capital; Skills; Occupational Choice; Labor Productivity
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10010836385