Valid Two-Step Identification-Robust Confidence Sets for GMM

• 成果类型: Article
• 署名作者: Andrews, Isaiah
• 署名单位: Massachusetts Institute of Technology (MIT)
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/REST_a_00682
• 发表日期: 2018-05
• 页码: 337-348
• 关键词: models; weak; tests

摘要

In models with potentially weak identification, researchers often decide whether to report a robust confidence set based on an initial assessment of model identification. Two-step procedures of this sort can generate large coverage distortions for reported confidence sets, and existing procedures for controlling these distortions are quite limited. This paper introduces a generally applicable approach to detecting weak identification and constructing two-step confidence sets in GMM. This approach controls coverage distortions under weak identification and indicates strong identification, with probability tending to 1 when the model is well identified.