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A FLEXIBLE NONPARAMETRIC TEST FOR CONDITIONAL INDEPENDENCE

Published online by Cambridge University Press:  02 September 2015

Meng Huang ,
Yixiao Sun  and
Halbert White
Meng Huang*
Affiliation:
Freddie Mac
Yixiao Sun*
Affiliation:
UC San Diego
Halbert White
Affiliation:
UC San Diego
*
*Address correspondence to Meng Huang, Freddie Mac, 1551 Park Run Dr., McLean, VA 22102, USA; email: nkhuangmeng@gmail.com or to Yixiao Sun, Department of Economics 0508, University of California, San Diego, La Jolla, CA 92093, USA; email: yisun@ucsd.edu.
*Address correspondence to Meng Huang, Freddie Mac, 1551 Park Run Dr., McLean, VA 22102, USA; email: nkhuangmeng@gmail.com or to Yixiao Sun, Department of Economics 0508, University of California, San Diego, La Jolla, CA 92093, USA; email: yisun@ucsd.edu.
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Abstract

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This paper proposes a nonparametric test for conditional independence that is easy to implement, yet powerful in the sense that it is consistent and achieves n−1/2 local power. The test statistic is based on an estimator of the topological “distance” between restricted and unrestricted probability measures corresponding to conditional independence or its absence. The distance is evaluated using a family of Generically Comprehensively Revealing (GCR) functions, such as the exponential or logistic functions, which are indexed by nuisance parameters. The use of GCR functions makes the test able to detect any deviation from the null. We use a kernel smoothing method when estimating the distance. An integrated conditional moment (ICM) test statistic based on these estimates is obtained by integrating out the nuisance parameters. We simulate the critical values using a conditional simulation approach. Monte Carlo experiments show that the test performs well in finite samples. As an application, we test an implication of the key assumption of unconfoundedness in the context of estimating the returns to schooling.

Type
ARTICLES
Information
Econometric Theory , Volume 32 , Issue 6 , December 2016 , pp. 1434 - 1482
Copyright
Copyright © Cambridge University Press 2015 

References

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