Fractional order statistic approximation for nonparametric conditional quantile inference

Matt Goldman and David Kaplan

Journal of Econometrics, 2017, vol. 196, issue 2, 331-346

Abstract: Using and extending fractional order statistic theory, we characterize the O(n−1) coverage probability error of the previously proposed (Hutson, 1999) confidence intervals for population quantiles using L-statistics as endpoints. 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)
Date: 2017
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Citations: View citations in EconPapers (7)

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Related works:
Working Paper: Fractional order statistic approximation for nonparametric conditional quantile inference (2016)
Working Paper: Fractional order statistic approximation for nonparametric conditional quantile inference (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:196:y:2017:i:2:p:331-346

DOI: 10.1016/j.jeconom.2016.09.015

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