Investigating finite sample properties of estimators for approximate factor models when N is small

We examine finite sample properties of estimators for approximate factor models when N is small. Contrary to the “rule-of-thumb”, we find that the principal component analysis estimator and the quasi-maximum likelihood estimator perform well even when N is small.

► We see performance of estimators for approximate factor models when N is small. ► According to the “rule-of-thumb”, they are thought to perform poorly for small N. ► However, Monte Carlo experiment shows that they perform well even when N is small.