Identification and Estimation of Regression Models with Misclassification
研究二元回归变量存在误分类时的非参数回归模型识别与估计问题，允许测量误差与回归变量相关，并提出了基于工具变量的核估计方法及误分类检验。
This paper studies the problem of identification and estimation in nonparametric regression models with a misclassified binary regressor where the measurement error may be correlated with the regressors. We show that the regression function is nonparametrically identified in the presence of an additional random variable that is correlated with the unobserved true underlying variable but unrelated to the measurement error. Identification for semiparametric and parametric regression functions follows straightforwardly from the basic identification result. We propose a kernel estimator based on the identification strategy, derive its large sample properties, and discuss alternative estimation procedures. We also propose a test for misclassification in the model based on an exclusion restriction that is straightforward to implement. Copyright The Econometric Society 2006.