Consistent Testing for Stochastic Dominance under General Sampling Schemes
提出一种子抽样方法，用于在一般依赖结构下检验一阶和二阶随机占优，允许序列相关和前景间依赖，并首次支持条件残差排序，检验具有一致性和渐近无偏性。
We propose a procedure for estimating the critical values of the extended Kolmogorov-Smirnov tests of First and Second Order Stochastic Dominance in the general K-prospect case. We allow for the observations to be serially dependent and, for the first time, we can accommodate general dependence amongst the prospects which are to be ranked. Also, the prospects may be the residuals from certain conditional models, opening the way for conditional ranking. We also propose a test of Prospect Stochastic Dominance. Our method is subsampling; we show that the resulting tests are consistent and powerful against some N(exp-1/2) local alternatives even when computed with a data-based subsample size. We also propose some heuristic methods for selecting subsample size and demonstrate in simulations that they perform reasonably. We show that our test is asymptotically similar on the entire boundary of the null hypothesis, and is unbiased. In comparison, any method based on resampling or simulating from the least favorable distribution does not have these properties and consequently will have less power against some alternatives.