Nonparametric Inference on Quantile Marginal Effects
Year Of Publication: 2014
Month Of Publication: August
Pages: 24
Download Count: 9
View Count: 1230
Comment Num: 0
Language: English
Source: working paper
Who Can Read: Free
Date: 8-26-2014
Publisher: Administrator
Summary
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.
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