Inference with Many Weak Instruments
针对线性工具变量模型中工具变量数量与样本量同速或更慢增长时的弱识别问题，提出了一种刀切版的安德森-鲁宾检验统计量，该统计量在异方差和弱识别下有效，并开发了相关的弱识别预检验。
Abstract We develop a concept of weak identification in linear instrumental variable 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. This new pre-test is valid under heteroscedasticity and with many instruments.