Journal Article

Nonparametric identification of regression models containing a misclassified dichotomous regressor without instruments


Xiaohong Chen, Yingyao Hu, Arthur Lewbel

Published Date

1 September 2008


Journal Article

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.