We build a simple diagnostic criterion for approximate factor structure in large panel datasets. Given observable factors, the criterion checks whether the errors are weakly cross-sectionally correlated or share at least one unobservable common factor (interactive effects). A general version...

This paper deals with estimation of high-dimensional covariance with a conditional sparsity structure, which is the composition of a low-rank matrix plus a sparse matrix. By assuming sparse error covariance matrix in a multi-factor model, we allow the presence of the cross-sectional correlation...

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...