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Fractional order statistic approximation for nonparametric conditional quantile inference

David Kaplan () and Matt Goldman

No 1502, Working Papers from Department of Economics, University of Missouri

Abstract: Using and extending fractional order statistic theory, we characterize the O(n−1) coverage probability error of the previously proposed confidence intervals for population quantiles using L-statistics as endpoints in Hutson (1999). We derive an analytic expression for the n−1 term, which may be used to calibrate the nominal coverage level to get O(n−3/2 [log(n)]3) coverage error. Asymptotic power is shown to be optimal. Using kernel smoothing, we propose a related method for nonparametric inference on conditional quantiles. This new method compares favorably with asymptotic normality and bootstrap methods in theory and in simulations. Code is provided for both unconditional and conditional inference.

Keywords: Dirichlet; high-order accuracy; inference-optimal bandwidth; kernel smoothing. (search for similar items in EconPapers)
JEL-codes: C21 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
Date: 2011-12-15, Revised 2016-06-14
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Forthcoming for publication at the Journal of Econometrics (URL: http://dx.doi.org/10.1016/j.jeconom.2016.09.015)

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https://economics.missouri.edu/working-papers/2015/wp1502_kaplan.pdf (application/pdf)

Related works:
Journal Article: Fractional order statistic approximation for nonparametric conditional quantile inference (2017) Downloads
Working Paper: Fractional order statistic approximation for nonparametric conditional quantile inference (2016) Downloads
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