Nonparametric Inference on Quantile Marginal Effects
David Kaplan
No 1413, Working Papers from Department of Economics, University of Missouri
Abstract:
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.
Keywords: fractional order statistics; high-order accuracy; nonseparable models (search for similar items in EconPapers)
JEL-codes: C21 (search for similar items in EconPapers)
Pages: 24 pgs.
Date: 2014-08-19
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (5)
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Persistent link: https://EconPapers.repec.org/RePEc:umc:wpaper:1413
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