Testing for causal e ffects in a generalized regression model with endogenous regressors
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Hausman_Testing for.pdf
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Author(s)
Abrevaya, Jason
Hausman, Jerry A.
Khan, Shakeeb
Date Issued
December 2010
Journal
Econometrica
Publisher
The Econometric Society
Citation
Abrevaya, J., Hausman, J. A. and Khan, S. (2010), Testing for Causal Effects in a Generalized Regression Model With Endogenous Regressors. Econometrica, 78: 2043–2061. doi: 10.3982/ECTA7133 © 2010 The Econometric Society
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Author's final manuscript
Abstract
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).
MIT Department
Massachusetts Institute of Technology. Department of Economics
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DOI of Published Version
https://doi.org/10.3982/ecta7133
