本文提出围绕一个非参数的定位功能，能够检测到一般的非参数的选择条件分布对称测试。该测试是开发一个通用串行依赖的背景下，创新的地方可能会出现一个未知的高阶序列依赖结构。检验统计量是一种功能性的非参数的残差和解释变量的联合经验分布，其可以检测非参数替代会聚到空的参数速率根-n的。临界值估计用自举技术容易实现的协助下，将所得测试的有效性的规律性的条件下正式有道理的。蒙特卡洛研究探讨了测试的有限样本性质。我们还调查损失是否超过给定的使用我们的测试方法股市的可用信息收益的可能性较大。以下是在职研究生讲座原文。

　　Testing Symmetry of a Nonparametric Conditional Distribution

　　This article proposes tests of symmetry of conditional distributions around a nonparametric location function able to detect general non-parametric alternatives. The test is developed in a general serial dependence context, where innovations may exhibit an unknown higher order serial dependence structure. The test statistic is a functional of the joint empirical distribution of non-parametric residuals and explanatory variables, which can detect non-parametric alternatives converging to the null at the parametric rate root-n. Critical values are estimated with the assistance of a bootstrap technique easy to implement, and the validity of the resulting test is formally justified under the regularity conditions. A Monte Carlo study examines the finite sample properties of the test. We also investigate whether losses are more likely than gains given the available information in stock markets using our testing approach.