Testing for Causal Effects in a Generalized Regression Model With Endogenous Regressors

• 成果类型: Article
• 署名作者: Abrevaya, Jason; Hausman, Jerry A.; Khan, Shakeeb
• 署名单位: University of Texas System; University of Texas Austin; Massachusetts Institute of Technology (MIT); Duke University
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA7133
• 发表日期: 2010
• 页码: 2043-2061
• 关键词: RANK CORRELATION ESTIMATOR; variables; identification; EQUATIONS

摘要

A unifying framework to test for causal effects in nonlinear models is proposed. We consider a generalized linear-index regression model with endogenous regressors and no parametric assumptions on the error disturbances. To test the significance of the effect of an endogenous regressor, we propose a statistic that is a kernel-weighted version of the rank correlation statistic (tau) of Kendall (1938). The semiparametric model encompasses previous cases considered in the literature (continuous endogenous regressors (Blundell and Powell (2003)) and a single binary endogenous regressor (Vytlacil and Yildiz (2007))), but the testing approach is the first to allow for (i) multiple discrete endogenous regressors, (ii) endogenous regressors that are neither discrete nor continuous (e.g., a censored variable), and (iii) an arbitrary mix of endogenous regressors (e.g., one binary regressor and one continuous regressor).