发表机构
Graduate School of Economics, University of Tokyo（东京大学大学院经济学研究科）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出剖面安德森-鲁宾检验，允许工具直接效应在一定范围内变化，在弱工具下控制错误拒绝并构建置信集，模拟和应用显示其在弱识别时保留更多不确定性。
AI 中文摘要
工具变量分析通常依赖于工具仅通过内生回归变量影响结果的假设。在许多应用中，研究者只能为工具的直接效应辩护一个合理的范围，而当工具较弱时，传统的敏感性分析可能不可靠。本文提出了剖面安德森-鲁宾（pAR）检验，该检验考虑预设范围内的所有直接效应，并且只要至少一个可接受的直接效应与数据一致，就保留候选效应。在维持的抽样假设下，该程序在不要求强工具的情况下，对每个兼容的候选者控制错误拒绝。本文提供了构建置信集的实际方法，并区分了实质性边界与由实际工具设计决定的边界。模拟以及对退休储蓄和教育回报的应用表明，当工具较强时，该程序类似于传统的敏感性分析，但当识别较弱时，它保留了显著更多的不确定性。
英文摘要
Instrumental variable analyses often rely on the assumption that instruments affect the outcome only through the endogenous regressor. In many applications, researchers can defend only a plausible range for direct effects of instruments, while conventional sensitivity analyses may be unreliable when instruments are weak. This paper proposes the profiled Anderson--Rubin (pAR) test, which considers all direct effects within a prespecified range and retains a candidate effect whenever at least one admissible direct effect is consistent with the data. Under the maintained sampling assumptions, the procedure controls false rejection for each compatible candidate without requiring strong instruments. The paper provides practical methods for constructing confidence sets and distinguishes substantive bounds from bounds tied to the realized instrument design. Simulations and applications to retirement saving and returns to schooling show that the procedure resembles conventional sensitivity analysis when instruments are strong but preserves substantially more uncertainty when identification is weak.
