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A Comparison of Estimation Methods for Dynamic Factor Models of Large Dimensions

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Author Info

  • George Kapetanios

    ()
    (Queen Mary, University of London)

  • Massimiliano Marcellino

    (Bocconi University)

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.

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Bibliographic Info

Paper provided by Queen Mary, University of London, School of Economics and Finance in its series Working Papers with number 489.

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Date of creation: Apr 2003
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Handle: RePEc:qmw:qmwecw:wp489

Note: A revised version is available at the personal homepage of George Kapetanios .
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Keywords: Factor models; Principal components; Subspace algorithms;

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  1. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2000. "Reference Cycles: The NBER Methodology Revisited," CEPR Discussion Papers 2400, C.E.P.R. Discussion Papers.
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