Improved quantile inference via fixed-smoothing asymptotics and Edgeworth expansion

David Kaplan

Journal of Econometrics, 2015, vol. 185, issue 1, 20-32

Abstract: To estimate a sample quantile’s variance, the quantile spacing method involves smoothing parameter m. When m,n→∞, the corresponding Studentized test statistic is asymptotically N(0,1). Holding m fixed instead, the asymptotic distribution contains the Edgeworth expansion term capturing the variance of the quantile spacing. Consequently, the fixed-m distribution is more accurate than the standard normal under both asymptotic frameworks. A testing-optimal m is proposed to maximize power subject to size control. In simulations, the new method controls size better than similar methods while maintaining good power. Throughout are results for two-sample quantile treatment effect inference. Code is available online.

Keywords: Fixed-smoothing; High-order accuracy; Hypothesis testing; Testing-optimal smoothing parameter (search for similar items in EconPapers)
JEL-codes: C21 (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (8)

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Working Paper: Improved Quantile Inference Via Fixed-Smoothing Asymptotics And Edgeworth Expansion (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:185:y:2015:i:1:p:20-32

DOI: 10.1016/j.jeconom.2014.08.011

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