This paper extends the linear grouped fixed effects (GFE) panel model to allow for heteroskedasticity from a discrete latent group variable. Key features of GFE are preserved, such as individuals belonging to one of a finite number of groups and group membership is unrestricted and estimated. Ignoring group heteroskedasticity may lead to poor classification, which is detrimental to finite sample bias and standard errors of estimators. I introduce the "weighted grouped fixed effects" (WGFE) estimator that minimizes a weighted average of group sum of squared residuals. I establish $\sqrt{NT}$-consistency and normality under a concept of group separation based on second moments. A test of group homoskedasticity is discussed. A fast computation procedure is provided. Simulations show that WGFE outperforms alternatives that exclude second moment information. I demonstrate this approach by considering the link between income and democracy and the effect of unionization on earnings.

翻译：本文扩展了线性分组固定效应（GFE）面板模型，以允许来自离散潜在分组变量的异方差性。GFE的关键特征得以保留，例如个体属于有限数量的组之一，且组别归属不受限制并需估计。忽略分组异方差可能导致分类效果不佳，这会损害估计量的有限样本偏差和标准误。我引入“加权分组固定效应”（WGFE）估计量，该估计量最小化组内残差平方和的加权平均值。基于二阶矩的分组分离概念，我建立了$\sqrt{NT}$-相合性和正态性。讨论了一种分组同方差性检验方法，并提供了快速计算程序。模拟表明，WGFE在性能上优于排除二阶矩信息的替代方法。我通过考察收入与民主之间的联系以及工会化对收入的影响来展示这一方法。