An Improved Bootstrap Test of Stochastic Dominance

Oliver Linton, Kyungchul Song and Yoon-Jae Whang
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Kyungchul Song: University of Pennsylvania

No 1713, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: We propose a new method of testing stochastic dominance that improves on existing tests based on the standard bootstrap or subsampling. The method admits prospects involving infinite as well as finite dimensional unknown parameters, so that the variables are allowed to be residuals from nonparametric and semiparametric models. The proposed bootstrap tests have asymptotic sizes that are less than or equal to the nominal level uniformly over probabilities in the null hypothesis under regularity conditions. This paper also characterizes the set of probabilities that the asymptotic size is exactly equal to the nominal level uniformly. As our simulation results show, these characteristics of our tests lead to an improved power property in general. The improvement stems from the design of the bootstrap test whose limiting behavior mimics the discontinuity of the original test's limiting distribution.

Keywords: Set estimation, Size of test, Similarity; Bootstrap, Subsampling (search for similar items in EconPapers)
JEL-codes: C12 C14 C52 (search for similar items in EconPapers)
Pages: 43 pages
Date: 2009-07
New Economics Papers: this item is included in nep-ecm and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Published in Journal of Econometrics (February 2010), 154(2): 186-202

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Related works:
Journal Article: An improved bootstrap test of stochastic dominance (2010)
Working Paper: An improved bootstrap test of stochastic dominance (2009)
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