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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- Gundersen, Craig & Kreider, Brent & Pepper, John, 2012.
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- Gundersen, Craig & Kreider, Brent & Pepper, John V., 2009. "The Impact of the National School Lunch Program on Child Health: A Nonparametric Bounds Analysis," Staff General Research Papers 13148, Iowa State University, Department of Economics.
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