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

Published

Journal Article

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). © 2010 The Econometric Society.

Full Text

Cited Authors

  • Abrevaya, J; Hausman, JA; Khan, S

Published Date

  • November 1, 2010

Published In

Volume / Issue

  • 78 / 6

Start / End Page

  • 2043 - 2061

Electronic International Standard Serial Number (EISSN)

  • 1468-0262

International Standard Serial Number (ISSN)

  • 0012-9682

Digital Object Identifier (DOI)

  • 10.3982/ECTA7133

Citation Source

  • Scopus