Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/62924
Authors: 
Kapetanios, George
Marcellino, Massimiliano
Year of Publication: 
2003
Series/Report no.: 
Working Paper, Department of Economics, Queen Mary, University of London 489
Abstract: 
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 methodology for estimating factors from large datasets based on state space models, discuss its theoretical properties and compare its performance with that of two alternative estimation approaches based, respectively, on static and dynamic principal components. The new method appears to perform best in recovering the factors in a set of simulation experiments, with static principal components a close second best. Dynamic principal components appear to yield the best fit, but sometimes there are leakages across the common and idiosyncratic components of the series. A similar pattern emerges in an empirical application with a large dataset of US macroeconomic time series.
Subjects: 
Factor models, Principal components, Subspace algorithms
JEL: 
C32
C51
E52
Document Type: 
Working Paper

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