We study linear regression models with clustered data, high-dimensional controls, and intricate exclusion restrictions. We propose a correctly centered internal instrument IV estimator that accommodates a broad class of exclusion restrictions and allows within-cluster dependence. The estimator admits a simple leave-out interpretation and is computationally tractable. We derive a central limit theorem for the associated quadratic form and propose a robust variance estimator. We also develop identification-robust inference procedures. Our framework extends dynamic panel methods to general clustered settings. We illustrate the approach in a large-scale fiscal intervention in rural Kenya, where spatial interference generates the exclusion-restriction pattern.

翻译：本文研究具有聚类数据、高维控制变量及复杂排除约束的线性回归模型。我们提出一种正确居中的内部工具变量IV估计量，该估计量能够适应广泛的排除约束类别并允许组内相关性。该估计量具有简洁的留一法解释且计算易处理。我们推导了相关二次型的中心极限定理，并提出稳健的方差估计量。同时开发了识别稳健的推断程序。本框架将动态面板方法扩展至一般聚类场景。我们通过在肯尼亚农村实施的大规模财政干预案例阐明该方法，其中空间干扰形成了排除约束模式。