Testing for causal e ffects in a generalized regression model with endogenous regressors
By Jason Abrevaya, Jerry A. Hausman, Shakeeb Khan. 2010
61 citations recorded by the source corpus.
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).
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/3625631512f56813db3969d6ad7a10e758cabb8e
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In Statistics and Probability
- A. Shaikh
- D. Chetverikov
- E. Vytlacil
- D. Wilhelm
- Victor Chernozhukov
- Kengo Kato
- James J. Heckman
- Alexandre Belloni
Citations in the corpus
- Partial Identification in Triangular Systems of Equations With Binary Dependent Variables (2011, background): “See also Abrevaya et al. (2010) , who in related analysis show how to identify and test for the direction of the effect of an endogenous regressor in a semiparametric model.”
- Nonparametric Instrumental Variable Estimation Under Monotonicity (2015, background): “…Y and the endogenous regressor X, given by the function g, but the main question of interest concerns not the exact shape of g itself, but whether the effect of X on Y , given by the slope of g, is positive, zero, or negative; see, for example, the discussion in Abrevaya, Hausman, and Khan (2010)).”