Agglomerative hierarchical clustering for selecting valid instrumental variables
提出一种将层次聚类与过度识别检验相结合的方法，从大量工具变量中筛选出有效的子集，允许存在多个内生回归变量，模拟显示性能优越，并应用于估计移民对工资的影响。
Summary We propose a procedure that combines hierarchical clustering with a test of overidentifying restrictions for selecting valid instrumental variables (IV) from a large set of IVs. Some of these IVs may be invalid in that they fail the exclusion restriction. We show that if the largest group of IVs is valid, our method achieves oracle properties. Unlike existing techniques, our work deals with multiple endogenous regressors. Simulation results suggest an advantageous performance of the method in various settings. The method is applied to estimating the effect of immigration on wages.