Testing for restricted stochastic dominance
AbstractAsymptotic and bootstrap tests are studied for testing whether there is a relation of stochastic dominance between two distributions. These tests have a null hypothesis of nondominance, with the advantage that, if this null is rejected, then all that is left is dominance. This also leads us to define and focus on restricted stochastic dominance, the only empirically useful form of dominance relation that we can seek to infer in many settings. One testing procedure that we consider is based on an empirical likelihood ratio. The computations necessary for obtaining a test statistic also provide estimates of the distributions under study that satisfy the null hypothesis, on the frontier between dominance and nondominance. These estimates can be used to perform dominance tests that can turn out to provide much improved reliability of inference compared with the asymptotic tests so far proposed in the literature.
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Stochastic dominance; empirical likelihood; bootstrap test;
Other versions of this item:
- Davidson, Russell & Duclos, Jean-Yves, 2006. "Testing for Restricted Stochastic Dominance," IZA Discussion Papers 2047, Institute for the Study of Labor (IZA).
- Russell Davidson & Jean-Yves Duclos, 2006. "Testing For Restricted Stochastic Dominance," Departmental Working Papers 2006-20, McGill University, Department of Economics.
- Russell Davidson & Jean-Yves Duclos, 2006. "Testing for Restricted Stochastic Dominance," Cahiers de recherche 0609, CIRPEE.
- Russell Davidson & Jean-Yves Duclos, 2006. "Testing for Restricted Stochastic Dominance," Working Papers 36, ECINEQ, Society for the Study of Economic Inequality.
- C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- I32 - Health, Education, and Welfare - - Welfare and Poverty - - - Measurement and Analysis of Poverty
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-01-16 (All new papers)
- NEP-ECM-2010-01-16 (Econometrics)
- NEP-LTV-2010-01-16 (Unemployment, Inequality & Poverty)
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