Fractional order statistic approximation for nonparametric conditional quantile inference

Year: 
2015
Working Paper Number: 
WP 15-02
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−3/2log(n)) 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 normaity and bootstrap methods in theory and in simulations. Code is provided for both unconditional and conditional inference.

JEL Codes: 
C21
Authors: 

Matt Goldman

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