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Set Identification of Generalized Linear Predictors in the Presence of Non-Classical Measurement Errors

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  • Kaspar W thrich

Abstract

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.

Suggested Citation

  • Kaspar W thrich, 2013. "Set Identification of Generalized Linear Predictors in the Presence of Non-Classical Measurement Errors," Diskussionsschriften dp1304, Universitaet Bern, Departement Volkswirtschaft.
    • Handle: RePEc:ube:dpvwib:dp1304
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    References listed on IDEAS

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    5. Brent Kreider & John V. Pepper & Craig Gundersen & Dean Jolliffe, 2012. "Identifying the Effects of SNAP (Food Stamps) on Child Health Outcomes When Participation Is Endogenous and Misreported," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 958-975, September.
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    More about this item

    Keywords

    Generalized linear predictor; Non-classical measurement error; Contaminated sampling; Corrupt sampling; Multiplicative mean independence; Stochastic dominance; Nonparametric bounds;
    All these keywords.

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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