Investigating Finite Sample Properties of Estimators for Approximate Factor Models When N Is Small
This paper examines the finite sample properties of estimators for approximate factor models when N is small via simulation study. Although the "rule-of-thumb" for factor models does not support using approximate factor models when N is small, we find that the principal component analysis estimator and quasi-maximum likelihood estimator proposed by Doz et al. (2008) perform very well even in this case. Our findings provide an opportunity for applying approximate factor models to low-dimensional data, which was thought to have been inappropriate for a long time.
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