A Parametric Estimation Method for Dynamic Factor Models of Large Dimensions

The estimation of dynamic factor models for large sets of variables has attracted
considerable attention recently, due to the increased availability of large datasets. In
this paper we propose a new parametric methodology for estimating factors from large
datasets based on state space models and discuss its theoretical properties. In particular,
we show that it is possible to estimate consistently the factor space. We also
develop a consistent information criterion for the determination of the number of factors
to be included in the model. Finally, we conduct a set of simulation experiments
that show that our approach compares well with existing alternatives.