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Nonparametric Identification of Regression Models Containing a Misclassified Dichotomous Regressor Without Instruments

Xiaohong Chen (), Yingyao Hu and Arthur Lewbel ()

No 675, Boston College Working Papers in Economics from Boston College Department of Economics

Abstract: This note considers nonparametric identification of a general nonlinear regression model with a dichotomous regressor subject to misclassification error. The available sample information consists of a dependent variable and a set of regressors, one of which is binary and error-ridden with misclassification error that has unknown distribution. Our identification strategy does not parameterize any regression or distribution functions, and does not require additional sample information such as instrumental variables, repeated measurements, or an auxiliary sample. Our main identifying assumption is that the regression model error has zero conditional third moment. The results include a closed-form solution for the unknown distributions and the regression function.

Keywords: misclassification error; identification; nonparametric regression (search for similar items in EconPapers)
JEL-codes: C20 C14 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
Date: 2007-07-31
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Journal Article: Nonparametric identification of regression models containing a misclassified dichotomous regressor without instruments (2008) Downloads
Working Paper: Nonparametric identification of regression models containing a misclassified dichotomous regressor without instruments (2007) Downloads
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