Testing for positive expectation dependence

• DOI: 10.1007/s10463-014-0492-7
• Publication Date: 2014-10-23T15:38:46Z
• Authors: Xuehu Zhu; Xu Guo; Lu Lin; Lixing Zhu
• Keywords: 0502 economics and business; 05 social sciences

In this paper, hypothesis testing for positive first-degree and higher-degree expectation dependence is investigated. Some tests of Kolmogorov–Smirnov type are constructed, which are shown to control type I error well and to be consistent against global alternative hypothesis. Further, the tests can also detect local alternative hypotheses distinct from the null hypothesis at a rate as close to the square root of the sample size as possible, which is the fastest possible rate in hypothesis testing. A nonparametric Monte Carlo test procedure is applied to implement the new tests because both sampling and limiting null distributions are not tractable. Simulation studies and a real data analysis are carried out to illustrate the performances of the new tests.