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.