Improved Quantile Inference Via Fixed-Smoothing Asymptotics And Edgeworth Expansion

David Kaplan

No 1313, Working Papers from Department of Economics, University of Missouri

Abstract: Estimation of a sample quantile's variance requires estimation of the probability density at the quantile. The common quantile spacing method involves smoothing parameter m. When m, n → ∞ , the corresponding Studentized test statistic asymptotically follows a standard normal distribution. Holding m fixed asymptotically yields a nonstandard distribution dependent on m that 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. For the fixed-m test, I propose an m to maximize power subject to size control, as calculated via Edgeworth expansion. Compared with similar methods, the new method controls size better and maintains good or better power in simulations. Results for two-sample quantile treatment effect inference are given in parallel.

Keywords: Edgeworth expansion; fixed-smoothing asymptotics; inference; quantile; studentize; testing-optimal (search for similar items in EconPapers)
JEL-codes: C01 C12 C21 (search for similar items in EconPapers)
Pages: 46 pgs.
Date: 2013-07-05
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Published in Journal of Econometrics, Volume 185, Issue 1, March 2015, Pages 20-32

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Journal Article: Improved quantile inference via fixed-smoothing asymptotics and Edgeworth expansion (2015)
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