Fractional order statistic approximation for nonparametric conditional quantile inference

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• 作者：Matt Goldman^a ; David M. Kaplan^b ; ^{kaplandm@Missouri.edu} ;
• 关键词：C21
• 刊名：Journal of Econometrics
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：196
• 期：2
• 页码：331-346
• 全文大小：1176 K
• 卷排序：196

文摘

Using and extending fractional order statistic theory, we characterize the O(n−1)O(n−1) coverage probability error of the previously proposed (Hutson, 1999) confidence intervals for population quantiles using LL-statistics as endpoints. We derive an analytic expression for the n−1n−1 term, which may be used to calibrate the nominal coverage level to get O(n−3/2[log(n)]3)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.