Testing for Restricted Stochastic Dominance: Some Further Results
AbstractExtensions are presented to the results of Davidson and Duclos (2007), whereby the null hypothesis of restricted stochastic non dominance can be tested by both asymptotic and bootstrap tests, the latter having considerably better properties as regards both size and power. In this paper, the methodology is extended to tests of higher-order stochastic dom- inance. It is seen that, unlike the first-order case, a numerical nonlinear optimisation prob- lem has to be solved in order to construct the bootstrap DGP. Conditions are provided for a solution to exist for this problem, and efficient numerical algorithms are laid out. The em- pirically important case in which the samples to be compared are correlated is also treated, both for first-order and for higher-order dominance. For all of these extensions, the boot- strap algorithm is presented. Simulation experiments show that the bootstrap tests perform considerably better than asymptotic tests, and yield reliable inference in moderately sized samples.
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Bibliographic InfoArticle provided by Rimini Centre for Economic Analysis in its journal Review of Economic Analysis.
Volume (Year): 1 (2009)
Issue (Month): 1 (September)
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Higher-order stochastic dominance; empirical likelihood; bootstrap test; corre- lated samples;
Other versions of this item:
- Russell Davidson, 2007. "Testing For Restricted Stochastic Dominances: Some Further Results," Departmental Working Papers 2007-15, McGill University, Department of Economics.
- Russell Davidson, 2009. "Testing for restricted stochastic dominance: some further results," Working Papers halshs-00443556, HAL.
- 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, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
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