DP5620 A Parametric Estimation Method for Dynamic Factor Models of Large Dimensions

Author(s): George Kapetanios, Massimiliano Marcellino
Publication Date: April 2006
Keyword(s): factor models, principal components, subspace algorithms
JEL(s): C32, C51, E52
Programme Areas: International Macroeconomics
Link to this Page: cepr.org/active/publications/discussion_papers/dp.php?dpno=5620

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.