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An improved bootstrap test of stochastic dominance

[journal article]

Linton, Oliver; Song, Kyungchul; Whang, Yoon-Jae

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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.
Classification Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods
Document language English
Publication Year 2009
Page/Pages p. 186-202
Journal Journal of Econometrics, 154 (2009) 2
Status Postprint; peer reviewed
Licence PEER Licence Agreement (applicable only to documents from PEER project)