Cluster Jackknife Instrumental Variables Estimation
提出聚类刀切工具变量估计量（CJIVE），通过剔除观测所在整个聚类来构造第一阶段预测值，消除多工具变量偏误，适用于传统线性模型和异质性处理效应下的局部平均处理效应估计。
Abstract Researchers commonly use jackknife-based instrumental variables estimators to eliminate the many-instruments bias of two-stage least squares. Where inference must be clustered, however, the jackknife fails to eliminate the bias. We propose a cluster-jackknife approach in which first-stage predicted values for each observation are constructed from a regression that leaves out the observation’s entire cluster. The cluster-jackknife instrumental variables estimator (CJIVE) eliminates many-instruments bias, and consistently estimates causal effects in the traditional linear model and local average treatment effects in the heterogeneous treatment effects framework. We illustrate the method in an application estimating the effects of pre-trial detention in Miami-Dade County.