Nonparametric identification of regression models containing a misclassified dichotomous regressor without instruments
Xiaohong Chen (),
Yingyao Hu and
Arthur Lewbel ()
No CWP17/07, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies
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.
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Journal Article: Nonparametric identification of regression models containing a misclassified dichotomous regressor without instruments (2008)
Working Paper: Nonparametric Identification of Regression Models Containing a Misclassified Dichotomous Regressor Without Instruments (2007)
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