This paper considers a linear panel model with interactive fixed effects and unobserved individual and time heterogeneities that are captured by some latent group structures and an unknown structural break, respectively. To enhance realism, the model may have different numbers of groups and/or different group memberships before and after the break. With preliminary nuclear norm regularized estimation followed by row- and column-wise linear regressions, we estimate the break point based on the idea of binary segmentation and the latent group structures together with the number of groups before and after the break by sequential testing K-means algorithm simultaneously. It is shown that the break point, the number of groups and the group memberships can each be estimated correctly with probability approaching one. Asymptotic distributions of the estimators of the slope coefficients are established. Monte Carlo simulations demonstrate excellent finite sample performance for the proposed estimation algorithm. An empirical application to real house price data across 377 Metropolitan Statistical Areas in the US from 1975 to 2014 suggests the presence both of structural breaks and of changes in group membership.

中文翻译：

---

具有时变潜在群体结构的面板数据模型

本文考虑了具有交互固定效应和未观察到的个体和时间异质性的线性面板模型，这些异质性分别由一些潜在的群体结构和未知的结构断裂捕获。为了增强真实感，模型在休息之前和之后可以具有不同数量的组和/或不同的组成员资格。通过初步的核范数正则化估计，然后进行行和列线性回归，我们基于二元分割的思想和潜在组结构以及通过顺序测试 K- 断裂前后的组数来估计断点。同时表示算法。结果表明，断点、组数和组成员资格均可以以接近 1 的概率正确估计。建立了斜率系数估计量的渐近分布。蒙特卡洛模拟证明了所提出的估计算法具有出色的有限样本性能。对 1975 年至 2014 年美国 377 个大都市统计区实际房价数据的实证应用表明，存在结构性断裂和群体成员变化。