符号一致性检验
Testing Sign Agreement
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中文总结 AI 辅助
本文针对有限个参数的符号一致性检验问题，提出两种适用于任意相依估计量的新检验，证明其能一致控制渐近显著性水平，并通过模拟验证了理论预测。
中文摘要 AI 辅助
本文研究有限个参数间的符号一致性检验问题，该问题出现于多种实证场景，例如检测子组、结果或时期间符号相反的处理效应，以及检验局部平均处理效应的工具变量有效性。针对“所有参数均非负或均非正”的原假设，本文提出两种新检验：最不利检验与条件检验。最不利检验采用最坏情况原假设临界值；条件检验先筛选出估计值为大的正值或负值的分量，再基于筛选事件对剩余符号未确定的分量进行检验。与现有符号一致性检验不同，两种方法均适用于估计量间存在任意相依性的情形；在估计量相互独立的特殊情况下，临界值仅取决于维度与检验水平。本文证明，两种检验在一大类非参数分布上均能一致控制渐近显著性水平；局部渐近功效分析揭示了一种权衡关系：最不利检验在符号约束起作用的边界配置附近功效更高，而条件检验在部分分量与零值充分分离时功效更高。模拟证据在有限样本中验证了这些理论预测。
英文摘要
This article considers the problem of testing sign agreement among a finite number of parameters. This problem arises in empirical settings such as detecting treatment effects with opposite signs across subgroups, outcomes, or time periods, and testing instrument validity for local average treatment effects. For the null hypothesis that the parameters are either all non-negative or all non-positive, I propose two novel tests: a least favorable test and a conditional test. The least favorable test uses a worst-case null critical value, while the conditional test first screens components with large positive or negative estimates and then tests the remaining sign-unresolved components conditional on the screening event. Unlike existing sign agreement tests, both procedures accommodate arbitrary dependence among estimators; in the special case of independent estimators, the critical values depend only on the dimension and testing levels. We show that both tests control asymptotic size uniformly over a large class of nonparametric distributions. Local asymptotic power analysis reveals a tradeoff: the least favorable test is more powerful near boundary configurations where sign restrictions bind, whereas the conditional test is more powerful when some components are well separated from zero. Simulation evidence supports these theoretical predictions in finite samples.
