We propose new break tests for parameters such as mean, variance, quantile and others of panel data sets, in a general setup based on the self-normalization method. The self-normalization tests show much better size than existing tests, resolving their over-size problem for panels with serial dependence, cross-sectional dependence, conditional heteroscedasticity and/or N relative larger than T, which is demonstrated theoretically by a nuisance parameter free limiting null distribution and experimentally by very stable finite sample sizes. The proposed test is also implemented much more easily than the existing tests in that the proposed test needs no bandwidth selection for the long-run variance estimation and is computed very simply. Applications of the self-normalization test to the financial stock return and realized volatility indicate more toward absence of breaks of mean and/or variance than the existing tests which neglect cross-sectional correlation and other features apparent in the data sets.

在基于自归一化方法的一般设置中，我们针对面板数据集的平均值、方差、分位数和其他参数提出了新的中断测试。自标准化测试显示出比现有测试更好的大小，解决了具有序列依赖性、横截面依赖性、条件异方差性和/或N相对大于T 的面板的过大问题，这在理论上通过无扰参数限制零分布和非常稳定的有限样本大小在实验上证明。建议的测试也比现有的测试更容易实现，因为建议的测试不需要为长期方差估计选择带宽，并且计算非常简单。与忽略横截面相关性和数据集中其他明显特征的现有测试相比，自标准化测试对金融股票回报和已实现波动率的应用表明，更倾向于不存在均值和/或方差的中断。