A note on statistical analysis of factor models of high dimension

Abstract

Linear factor models are familiar tools used in many fields. Several pioneering literatures established foundational theoretical results of the quasi-maximum likelihood estimator for high-dimensional linear factor models. Their results are based on a critical assumption: The error variance estimators are uniformly bounded in probability. Instead of making such an assumption, we provide a rigorous proof of this result under some mild conditions.