Improved penalization for determining the number of factors in approximate factor models

Abstract

The procedure proposed by Bai and Ng (2002) for identifying the number of factors in static factor models is revisited. In order to improve its performance, we introduce a tuning multiplicative constant in the penalty, an idea that was proposed by Hallin and Liška (2007) in the context of dynamic factor models. Simulations show that our method in general delivers more reliable estimates, in particular in the case of large idiosyncratic disturbances. Keywords: Number of factors; Approximate factor models; Information criterion; Model selectio