We study kmeans clustering estimation of panel data models with a latent group structure and $N$ units and $T$ time periods under long panel asymptotics. We show that the group-specific coefficients can be estimated at the parametric $\sqrt{NT}$-rate even if error variances diverge as $T\to \infty$ and consequently some units are asymptotically misclassified. This limit case approximates empirically relevant settings and is not covered by existing asymptotic results.

中文翻译：

---

分类错误的聚类面板模型估计量的收敛速度

我们研究具有潜在群体结构的面板数据模型的kmeans聚类估计和$ñ$ 单位和 $Ť$长时间面板无症状的时间段。我们表明可以在参数下估计特定于组的系数$\sqrt{ñŤ}$率，即使误差方差发散为 $Ť\to \infty$因此，某些单位会被渐进式错误分类。该极限情况近似于经验相关的设置，并且不存在于现有的渐近结果中。