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A parametric estimation method for dynamic factor models of large dimensions

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  • George Kapetanios
  • Massimiliano Marcellino

Abstract

The estimation of dynamic factor models for large sets of variables has attracted considerable attention recently, because of the increased availability of large data sets. In this article we propose a new parametric methodology for estimating factors from large data sets 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 conduct a set of simulation experiments that show that our approach compares well with existing alternatives. Copyright 2009 The Authors. Journal compilation 2009 Blackwell Publishing Ltd

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Bibliographic Info

Article provided by Wiley Blackwell in its journal Journal of Time Series Analysis.

Volume (Year): 30 (2009)
Issue (Month): 2 (03)
Pages: 208-238

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Handle: RePEc:bla:jtsera:v:30:y:2009:i:2:p:208-238

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  8. Sin, Chor-Yiu & White, Halbert, 1996. "Information criteria for selecting possibly misspecified parametric models," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 207-225.
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  16. George Kapetanios & Massimiliano Marcellino, 2003. "A Comparison of Estimation Methods for Dynamic Factor Models of Large Dimensions," Working Papers 489, Queen Mary, University of London, School of Economics and Finance.
  17. Carlo Ambrogio Favero & Massimilano Marcellino & Francesca Neglia, . "Principal components at work: The empirical analysis of monetary policy with large datasets," Working Papers 223, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
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