论文标题
群集模型的估计量的收敛速率错误分类
Convergence rate of estimators of clustered panel models with misclassification
论文作者
论文摘要
我们研究具有潜在组结构和$ n $单位的面板数据模型的Kmeans聚类估计，在长面板渐近学下，$ n $单位和$ t $时间段。我们表明，即使误差方差为$ t \ to \ infty $，并且某些单位渐近错误地分类，也可以在参数root $ nt $速率上估算组的系数。该限制案例近似于经验相关的设置，并未被现有的渐近结果所涵盖。
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 root $NT$ rate even if error variances diverge as $T \to \infty$ and some units are asymptotically misclassified. This limit case approximates empirically relevant settings and is not covered by existing asymptotic results.
