A note on statistical analysis of factor models of high dimension
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.
|Work Title||A note on statistical analysis of factor models of high dimension|
|License||In Copyright (Rights Reserved)|
|Publication Date||August 1, 2021|
|Publisher Identifier (DOI)||
|Deposited||November 15, 2021|
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