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

Gregory Cox

Papers from arXiv.org

Abstract: This paper describes how to reparameterize 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 reparameterization to distinguish weakly identified parameters from strongly identified parameters. The reparameterizations 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 parameterization. An empirical application to a factor model of parental investments in children is included.

Date: 2022-11, Revised 2024-03
New Economics Papers: this item is included in nep-dcm and nep-ecm
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2211.00329

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