Identification and estimation of regression models with misclassification

• 成果类型: Article
• 署名作者: Mahajan, A
• 署名单位: Stanford University
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.1111/j.1468-0262.2006.00677.x
• 发表日期: 2006
• 页码: 631-665
• 关键词: instrumental variable estimation; measurement error; semiparametric estimation; CHOICE

摘要

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 serniparametric 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.