Weak Identification in Low-Dimensional Factor Models with One or Two Factors

• 成果类型: Article
• 署名作者: Cox, Gregory Fletcher
• 署名单位: National University of Singapore
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/rest_a_01441
• 发表日期: 2026
• 关键词: time-series models; inference; likelihood; tests; TECHNOLOGY; RECOVERY; gmm

摘要

This paper describes how to reparametrize low-dimensional factor models with one or two factors to fit weak identification theory developed for generalized method of moments models. Some identification-robust tests, here called plug-in tests, require a reparametrization to distinguish weakly identified parameters from strongly identified parameters. The reparametrizations in this paper make plug-in tests available for subvector hypotheses in low-dimensional factor models with one or two factors. Simulations show that the plug-in tests are less conservative than identification-robust tests that use the original parametrization. An empirical application to a factor model of parental investments in children is included.