An Improved Bootstrap Test For Restricted Stochastic Dominance

Thomas M. Lok and Rami Tabri ()

No 2015-15, Working Papers from University of Sydney, School of Economics

Abstract: Bootstrap Testing for restricted stochastic dominance of a pre-specified order between two distributions is of interest in many areas of economics. This paper develops a new method for improving the performance of such tests that employ a moment selection procedure: tilting the empirical distribution in the moment selection procedure. We propose that the amount of tilting be chosen to maximize the empirical likelihood subject to the restrictions of the null hypothesis, which are a continuum of un-conditional moment inequality conditions. We characterize sets of population distributions on which a modified test is (i) asymptotically equivalent to its non-modified version to first-order, and (ii) superior to its non-modified version according to local power when the sample size is large enough. We report simulation results that show the modified versions of leading tests are noticeably less conservative than their non-modified counterparts and have improved power. Finally, an empirical example is discussed to illustrate the proposed method. improved power. Finally, an empirical example is discussed to illustrate the proposed method.

Keywords: Bootstrap Test; Contact Set; Empirical Likelihood; Semi-Infinite Program; Restricted Stochastic Dominance. (search for similar items in EconPapers)
Date: 2015-06, Revised 2019-08
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 (2)

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