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ON USING LINEAR QUANTILE REGRESSIONS FOR CAUSAL INFERENCE
Published online by Cambridge University Press: 23 May 2016
Abstract
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.
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- 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.
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