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ON USING LINEAR QUANTILE REGRESSIONS FOR CAUSAL INFERENCE

Published online by Cambridge University Press:  23 May 2016

Ryutah Kato  and
Yuya Sasaki
Ryutah Kato*
Affiliation:
Johns Hopkins
Yuya Sasaki*
Affiliation:
Johns Hopkins
*
*Address correspondence to Ryutah Kato and Yuya Sasaki, Johns Hopkins University, Department of Economics, Wyman Park Building 544E, 3400 N. Charles St., Baltimore, MD 21218, USA; e-mail: [email protected] and [email protected].
*Address correspondence to Ryutah Kato and Yuya Sasaki, Johns Hopkins University, Department of Economics, Wyman Park Building 544E, 3400 N. Charles St., Baltimore, MD 21218, USA; e-mail: [email protected] and [email protected].
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Abstract

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We show that the slope parameter of the linear quantile regression measures a weighted average of the local slopes of the conditional quantile function. Extending this result, we also show that the slope parameter measures a weighted average of the partial effects for a general structural function. Our results support the use of linear quantile regressions for causal inference in the presence of nonlinearity and multivariate unobserved heterogeneity. The same conclusion applies to linear regressions.

Type
ARTICLES
Information
Econometric Theory , Volume 33 , Issue 3 , June 2017 , pp. 664 - 690
Copyright
Copyright © Cambridge University Press 2016 

Footnotes

We were benefited from useful suggestions from V. Chernozhukov, I. Fernández-Val, and K. Kato. All the remaining errors are ours.

References

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