In this talk we briefly review estimation methods in the dynamic factor model, and propose an information criterion for determining the number
\(q\) of factors in the general model developed by Forni et al. (2000), as opposed to the static and restricted dynamic models considered in Bai and Ng (2002, 2005) or Amengual and Watson (2006). Our criterion is based on the fact that this number q is also the number of diverging eigenvalues of the spectral density matrix of the observations as the cross-sectional dimension n goes to infinity. We provide sufficient conditions for consistency of the criterion for large
\(T\) is the series length). We show how the method can be implemented, and provide simulations and empirics illustrating its excellent finite sample performance. Application to real data brings some new empirical contribution in the ongoing debate on the number of factors driving the US economy. This is joint work with Roman Liska.
La présentation sera faite en français avec transparents en anglais.