Principal components estimation and identification of static factors
Jushan Bai, Serena Ng – 2013
Abstract
It is known that the principal component estimates of the factors and the loadings are rotations of the underlying latent factors and loadings. We study conditions under which the latent factors can be estimated asymptotically without rotation. We derive the limiting distributions for the estimated factors and factor loadings when N and T are large and make precise how identification of the factors affects inference based on factor augmented regressions. We also consider factor models with additive individual and time effects. The asymptotic analysis can be modified to analyze identification schemes not considered in this analysis. © 2013 Elsevier B.V. All rights reserved.
Another description of the named factor normalization covered in @stockDynamicFactorModels2016. Think I get it at this point, though I somewhat question its utility