Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/79369
Authors: 
Chen, Xiaohong
Hu, Yingyao
Lewbel, Arthur
Year of Publication: 
2007
Series/Report no.: 
cemmap working paper, Centre for Microdata Methods and Practice CWP17/07
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.
Subjects: 
misclassification error , identification , nonparametric regression
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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