Inference with Many Weak Instruments

• 成果类型: Article
• 署名作者: Mikusheva, Anna; Sun, Liyang
• 署名单位: Massachusetts Institute of Technology (MIT); University of California System; University of California Berkeley
• 刊物名称: REVIEW OF ECONOMIC STUDIES
• ISSN/ISSBN: 0034-6527
• DOI: 10.1093/restud/rdab097
• 发表日期: 2022
• 页码: 2663-2686
• 关键词: regression; tests

摘要

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.