%0 Journal Article %A Han, Xu %A Caner, Mehmet %D 2017 %T Determining the number of factors with potentially strong within-block correlations in error terms %U https://tandf.figshare.com/articles/journal_contribution/Determining_the_number_of_factors_with_potentially_strong_within-block_correlations_in_error_terms/4787836 %R 10.6084/m9.figshare.4787836.v1 %2 https://ndownloader.figshare.com/files/7869304 %K Factor model %K model selection %K C33 %K C52 %X

We develop methods to estimate the number of factors when error terms have potentially strong correlations in the cross-sectional dimension. The information criteria proposed by Bai and Ng (2002) require the cross-sectional correlations between the error terms to be weak. Violation of this weak correlation assumption may lead to inconsistent estimates of the number of factors. We establish two data-dependent estimators that are consistent whether the error terms are weakly or strongly correlated in the cross-sectional dimension. To handle potentially strong cross-sectional correlations between the error terms, we use a block structure in which the within-block correlation may either be weak or strong, but the between-block correlation is limited. Our estimators allow imperfect knowledge and a moderate misspecification of the block structure. Monte-Carlo simulation results show that our estimators perform similarly to existing methods for cases in which the conventional weak correlation assumption is satisfied. When the error terms have a strong cross-sectional correlation, our estimators outperform the existing methods.

%I Taylor & Francis