A Practical Note on the Determination of the Number of Factors Using Information Criteria with Data-Driven Penalty

As is well known, when using an information criterion to select the number of common factors in factor models the appropriate penalty is generally indetermine in the sense that it can be scaled by an arbitrary constant, c say, without affecting consistency. In an influential paper, Hallin and Liˇska (Determining the Number of Factors in the General Dynamic Factor Model, Journal of the American Statistical Association 102, 603–617, 2007)proposes a data-driven procedure for selecting the appropriate value of c. However, by removing one source of indeterminacy, the new procedure simultaneously creates several new ones,which make for rather complicated implementation, a problem that has been largely overlooked in the literature. By providing an extensive analysis using both simulated and real data, the current paper fills this gap.