标题： 一种与维度无关的自助法安德森-鲁宾检验用于工具变量回归 显示英文标题

标题： A Dimension-Agnostic Bootstrap Anderson-Rubin Test For Instrumental Variable Regressions

摘要： 弱识别稳健的Anderson-Rubin（AR）检验用于工具变量（IV）回归，通常根据工具变量的数量是否被视为固定或随着样本量增加而分别开发。这些检验依赖于不同的检验统计量和临界值。要应用它们，研究人员必须对工具变量数量的渐近行为做出判断，这在工具变量数量适中时可能不明确。在本文中，我们提出了一种基于引导的、维度无关的AR检验。通过推导检验统计量及其引导对应量的强逼近，我们证明了我们的新检验在工具变量数量固定或增加的情况下都具有正确的渐近大小——允许但不要求工具变量数量超过样本量。我们还分析了在工具变量数量固定和增加的情况下，所提出的统一有效检验的幂性质。 显示英文摘要

摘要： Weak-identification-robust Anderson-Rubin (AR) tests for instrumental variable (IV) regressions are typically developed separately depending on whether the number of IVs is treated as fixed or increasing with the sample size. These tests rely on distinct test statistics and critical values. To apply them, researchers are forced to take a stance on the asymptotic behavior of the number of IVs, which can be ambiguous when the number is moderate. In this paper, we propose a bootstrap-based, dimension-agnostic AR test. By deriving strong approximations for the test statistic and its bootstrap counterpart, we show that our new test has a correct asymptotic size regardless of whether the number of IVs is fixed or increasing -- allowing, but not requiring, the number of IVs to exceed the sample size. We also analyze the power properties of the proposed uniformly valid test under both fixed and increasing numbers of IVs.