论文标题
推断许多薄弱的乐器
Inference with Many Weak Instruments
论文作者
论文摘要
我们在线性IV模型中开发了一个弱识别概念，其中仪器的数量比样本量相同或较慢。我们提出了经典弱识别识别的安德森·罗宾（AR）测试统计量的jackknif版本。基于Jackknifed AR的大样本推断在异方差和较弱的鉴定下是有效的。该统计量的可行版本使用了一种新颖的方差估计器。该测试具有统一的尺寸和良好的功率特性。我们还为弱识别提供了预测试，该测试与基于Jacknife Instrumental变量估计器（JIVE）的WALD测试的尺寸特性有关。这项新的预测试在异质性和许多乐器下有效。
We develop a concept of weak identification in linear IV models in which the number of instruments can grow at the same rate or slower than the sample size. We propose a jackknifed version of the classical weak identification-robust Anderson-Rubin (AR) test statistic. Large-sample inference based on the jackknifed AR is valid under heteroscedasticity and weak identification. The feasible version of this statistic uses a novel variance estimator. The test has uniformly correct size and good power properties. We also develop a pre-test for weak identification that is related to the size property of a Wald test based on the Jackknife Instrumental Variable Estimator (JIVE). This new pre-test is valid under heteroscedasticity and with many instruments.
