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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)

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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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Publisher Info
Paper provided by Queen Mary, University of London, Department of Economics 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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Related research
Keywords: Factor models Principal components Subspace algorithms

Other versions of this item:

Find related papers by JEL classification:
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models
C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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  1. Nektarios Aslanidis & Andrea Cipollini, 2007. "Leading indicator properties of the US corporate spreads," Money Macro and Finance (MMF) Research Group Conference 2006 115, Money Macro and Finance Research Group. [Downloadable!]
  2. Andreas Beyer & Roger E. A. Farmer & Jérôme Henry & Massimiliano Marcellino, 2005. "Factor analysis in a New-Keynesian model," Working Paper Series 510, European Central Bank. [Downloadable!]
    Other versions:
  3. Eickmeier, Sandra, 2004. "Business Cycle Transmission from the US to Germany : a Structural Factor Approach," Discussion Paper Series 1: Economic Studies 2004,12, Deutsche Bundesbank, Research Centre. [Downloadable!]
    Other versions:
  4. Stéphane Dées & Matthias Burgert, 2008. "Forecasting world trade. Direct versus "bottom-up" approaches," Working Paper Series 882, European Central Bank. [Downloadable!]
  5. Massimiliano Marcellino & George Kapetanios, . "The Role of Search Frictions and Bargaining for Inflation Dynamics," Working Papers 305, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University. [Downloadable!]
  6. Kapetanios, George & Marcellino, Massimiliano, 2006. "Impulse Response Functions from Structural Dynamic Factor Models: A Monte Carlo Evaluation," CEPR Discussion Papers 5621, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
    Other versions:
  7. Eickmeier, Sandra, 2005. "Common stationary and non-stationary factors in the euro area analyzed in a large-scale factor model," Discussion Paper Series 1: Economic Studies 2005,02, Deutsche Bundesbank, Research Centre. [Downloadable!]
  8. Todd E. Clark, 2003. "Disaggregate evidence on the persistence of consumer price inflation," Research Working Paper RWP 03-11, Federal Reserve Bank of Kansas City. [Downloadable!]
    Other versions:
  9. Andreas Beyer & Roger E. A. Farmer & Jérôme Henry & Massimiliano Marcellino, 2007. "Factor Analysis in a Model with Rational Expectations," NBER Working Papers 13404, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
    Other versions:
  10. Laurent Maurin & Matthieu Darracq Pariès, 2008. "The role of country-specific trade and survey data in forecasting euro area manufacturing production. Perspective from Large Panel factor models," Working Paper Series 894, European Central Bank. [Downloadable!]
  11. Jan J. J. Groen & George Kapetanios, 2008. "Revisiting useful approaches to data-rich macroeconomic forecasting," Staff Reports 327, Federal Reserve Bank of New York. [Downloadable!]
    Other versions:
  12. Kapetanios, George & Marcellino, Massimiliano, 2006. "A Parametric Estimation Method for Dynamic Factor Models of Large Dimensions," CEPR Discussion Papers 5620, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
  13. Günter W. Beck & Kirstin Hubrich & Massimiliano Marcellino, 2006. "Regional inflation dynamics within and across euro area countries and a comparison with the US," Working Paper Series 681, European Central Bank. [Downloadable!]
  14. Lucia Alessi & Matteo Barigozzi & Marco Capasso, 2006. "A Dynamic Factor Analysis of Business Cycle on Firm-Level Data," LEM Papers Series 2006/27, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy. [Downloadable!]
  15. Andrea Cipollini & George Kapetanios, 2006. "Forecasting Financial Crises and Contagion in Asia using Dynamic Factor Analysis," Computing in Economics and Finance 2006 477, Society for Computational Economics. [Downloadable!]
  16. Eickmeier, Sandra & Breitung, Jörg, 2005. "How synchronized are central and east European economies with the euro area? : Evidence from a structural factor model," Discussion Paper Series 1: Economic Studies 2005,20, Deutsche Bundesbank, Research Centre. [Downloadable!]
  17. Guenter Beck & Massimiliano Marcellino, 2006. "Regional Inflation Dynamics within and across Euro Area and a Comparison with the US," Computing in Economics and Finance 2006 338, Society for Computational Economics. [Downloadable!]
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