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Nonparametric Inference on Quantile Marginal Effects
Year Of Publication: 2014
Month Of Publication: August
Pages: 24
Download Count: 9
View Count: 1694
Comment Num: 0
Language: English
Source: working paper
Who Can Read: Free
Date: 8-26-2014
Publisher: Administrator
We propose a nonparametric method to construct confidence intervals for quantile marginal effects (i.e., derivatives of the conditional quantile function). Under certain conditions, a quantile marginal effect equals a causal (structural) effect in a general nonseparable model, or equals an average thereof within a particular subpopulation. The high-order accuracy of our method is derived. Simulations and an empirical example demonstrate the new method's favorable performance and practical use. Code for the new method is provided.
Kaplan, David M. Sign in to follow this author
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