This paper investigates change-point of variance in panel data models with time series of $α$-mixing. Based on the cumulative sum (CUSUM) method and the individual differences, we construct a CUSUM test for panel data models to detect variance changes. Under the null hypothesis, we derive the limit distribution of this test, which can be used to detect the change-point of variance. Under the alternative hypothesis, the limit behavior of the CUSUM test is also derived. To validate the performance of the test, we conducted simulation analyses on with Gaussian and Gamma errors. The results demonstrate that this testing method significantly outperforms existing approaches, particularly in detecting sparse variance changes. Finally, we conducted a practical case study using panel data from the Shanghai Shenzhen CSI 300 Index Components. Not only did we successfully identify the change-points of variance, but we also delved deeper into the underlying economic drivers behind these changes.

翻译：本文研究具有$α$-混合时间序列的面板数据模型中的方差变点问题。基于累积和（CUSUM）方法与个体差异，我们构建了适用于面板数据模型的CUSUM检验以侦测方差变化。在原假设下，我们推导了该检验的极限分布，该分布可用于检测方差变点。在备择假设下，本文亦推导了CUSUM检验的极限性质。为验证检验性能，我们对高斯误差与伽马误差情形进行了模拟分析。结果表明该检验方法显著优于现有方案，尤其在侦测稀疏方差变化方面表现突出。最后，我们使用沪深300指数成分股的面板数据开展了实证案例研究。不仅成功识别了方差变点，还深入探究了这些变化背后的经济驱动因素。