Weak Identification in Low-Dimensional Factor Models with One or Two Factors
Econometrics
2024-03-08 v2
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.
Keywords
Cite
@article{arxiv.2211.00329,
title = {Weak Identification in Low-Dimensional Factor Models with One or Two Factors},
author = {Gregory Cox},
journal= {arXiv preprint arXiv:2211.00329},
year = {2024}
}