A large number of existing high-dimensional panel data analyses are established based on normal or nearly normal distribution assumptions, which may be not robust to severe departures of normality. Since the observed data may not follow the normal distribution in some specific applications, it is necessary to design robust tests to departures of normality. On this ground, we propose a rank-based score test for testing slope homogeneity in high-dimensional panel data regressions, where robust tests to departures of normality are still rare. Both theoretical and numerical results demonstrate the advantage of the proposed test in robustness to departures of normality.

大量现有的高维面板数据分析是基于正态或接近正态分布的假设建立的，这些假设对于严重偏离正态性可能不具有鲁棒性。由于在某些特定应用中观察到的数据可能不遵循正态分布，因此有必要设计对正态性偏离的稳健测试。在此基础上，我们提出了一种基于等级的评分测试，用于测试高维面板数据回归中的斜率同质性，其中对正态性偏离的稳健测试仍然很少见。理论和数值结果都证明了所提出的测试在对正态性偏离的稳健性方面的优势。