Bootstrap Tests of Stochastic Dominance with Asymptotic Similarity on the Boundary

Oliver Lintoni (), Kyungchul Song () and Yoon-Jae Whang
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Oliver Lintoni: Department of Economics, London School of Economics
Kyungchul Song: Department of Economics, University of Pennsylvania
Authors registered in the RePEc Author Service: Oliver Bruce Linton

PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania

Abstract: We propose a new method of testing stochastic dominance which improves on existing tests based on bootstrap or subsampling. Our test requires estimation of the contact sets between the marginal distributions. Our tests have asymptotic sizes that are exactly equal to the nominal level uniformly over the boundary points of the null hypothesis and are therefore valid over the whole null hypothesis. We also allow the prospects to be indexed by infinite as well as finite dimensional unknown parameters, so that the variables may be residuals from nonparametric and semiparametric models. Our simulation results show that our tests are indeed more powerful than the existing subsampling and recentered bootstrap.

Keywords: Set estimation; Size of test; Unbiasedness; Similarity; Bootstrap; Subsampling (search for similar items in EconPapers)
JEL-codes: C12 C14 C52 (search for similar items in EconPapers)
Pages: 44 pages
Date: 2008-02-19
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 (6)

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Related works:
Working Paper: Bootstrap Tests of Stochastic Dominance with AsymptoticSimilarity on the Boundary (2008)
Working Paper: Bootstrap tests of stochastic dominance with asymptotic similarity on the boundary (2008)
Working Paper: Bootstrap tests of stochastic dominance with asymptotic similarity on the boundary (2008)
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Persistent link: https://EconPapers.repec.org/RePEc:pen:papers:08-006

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