A Robust Entropy-Based Test of Asymmetry for Discrete and Continuous Processes
提出一种基于熵的检验统计量，通过非参数密度估计检测离散和连续过程的不对称性，模拟显示其良好的检验水平和功效，应用于Nelson-Plosser数据表明优于现有检验。
We consider a metric entropy capable of detecting deviations from symmetry that is suitable for both discrete and continuous processes. A test statistic is constructed from an integrated normed difference between nonparametric estimates of two density functions. The null distribution (symmetry) is obtained by resampling from an artificially lengthened series constructed from a rotation of the original series about its mean (median, mode). Simulations demonstrate that the test has correct size and good power in the direction of interesting alternatives, while applications to updated Nelson and Plosser (1982) data demonstrate its potential power gains relative to existing tests.