We propose a refinement of the criterion by Bai and Ng  for determining the number of static factors in factor models with large datasets. It consists in multi-plying the penalty function by a constant which tunes the penalizing power of the function itself as in the Hallin and Liška  criterion for the number of dynamic factors. By iteratively evaluating the criterion for different values of this constant, we achieve more robust results than in the case of fixed penalty function. This is shown by means of Monte Carlo simulations on seven data generating processes, including heteroskedastic processes, on samples of different size. Two empirical applications are carried out on a macroeconomic and a financial dataset.
Approximate factor models Information criterion Number of factors