论文标题
分组固定效应模型的简单且计算的琐碎估计器
A Simple and Computationally Trivial Estimator for Grouped Fixed Effects Models
论文作者
论文摘要
本文为线性面板数据模型引入了一个新的固定效应估计器，该模型具有未观察到的异质性的聚类时间模式。该方法通过结合坡度系数的初步一致估计器，横截面单元的聚集成对分化聚类以及汇总的普通最小二乘回归的结合，避免了非凸和组合优化。在$ n $和$ n $和$ t $近的Infinity的框架内建立了渐近保证。与大多数现有方法不同，所提出的估计器在计算上是直接的，并且不需要在组数量上的已知上限。如有现有方法，此方法会导致对良好分离的组的一致估计，并逐渐估算出渐近参数的估计值，等同于对真实组的不可行的回归控制。申请重新审视了收入与民主之间的统计关联。
This paper introduces a new fixed effects estimator for linear panel data models with clustered time patterns of unobserved heterogeneity. The method avoids non-convex and combinatorial optimization by combining a preliminary consistent estimator of the slope coefficient, an agglomerative pairwise-differencing clustering of cross-sectional units, and a pooled ordinary least squares regression. Asymptotic guarantees are established in a framework where $T$ can grow at any power of $N$, as both $N$ and $T$ approach infinity. Unlike most existing approaches, the proposed estimator is computationally straightforward and does not require a known upper bound on the number of groups. As existing approaches, this method leads to a consistent estimation of well-separated groups and an estimator of common parameters asymptotically equivalent to the infeasible regression controlling for the true groups. An application revisits the statistical association between income and democracy.
