Identification of Instrumental Variable Correlated Random Coefficients Models

• 成果类型: Article
• 署名作者: Masten, Matthew A.; Torgovitsky, Alexander
• 署名单位: Duke University; Northwestern University
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/REST_a_00603
• 发表日期: 2016-12
• 页码: 1001-1005
• 关键词: simultaneous-equations models; least-squares estimation; nonseparable models; response models; average

摘要

We study identification and estimation of the average partial effect in an instrumental variable correlated random coefficients model with continuously distributed endogenous regressors. This model allows treatment effects to be correlated with the level of treatment. The main result shows that the average partial effect is identified by averaging coefficients obtained from a collection of ordinary linear regressions that condition on different realizations of a control function. These control functions can be constructed from binary or discrete instruments, which may affect the endogenous variables heterogeneously. Our results suggest a simple estimator that can be implemented with a companion Stata module.