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The generalised dynamic factor model: one sided estimation and forecasting

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  • Mario Forni
  • Marc Hallin
  • Marco Lippi
  • Lucrezia Reichlin

Abstract

This Paper proposes a new forecasting method that exploits information from a large panel of time series. The method is based on the generalized dynamic factor model proposed in Forni, Hallin, Lippi, and Reichlin (2000), and takes advantage of the information on the dynamic covariance structure of the whole panel. We first use our previous method to obtain an estimation for the covariance matrices of common and idiosyncratic components. The generalized eigenvectors of this couple of matrices are then used to derive a consistent estimate of the optimal forecast, which is constructed as a linear combination of present and past observations only (one-sided filter). This two-step approach solves the end-of-sample problems caused by two-sided filtering (as in our previous work), while retaining the advantages of an estimator based on dynamic information. Both simulation results and an empirical illustration on the forecast of the Euro area industrial production and inflation, based on a panel of 447 monthly time series show very encouraging results.

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Bibliographic Info

Paper provided by ULB -- Universite Libre de Bruxelles in its series ULB Institutional Repository with number 2013/10129.

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Date of creation: Sep 2005
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Publication status: Published in: American Statistical Association. Journal (2005) v.100 n° 471,p.830-840
Handle: RePEc:ulb:ulbeco:2013/10129

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Web page: http://difusion.ulb.ac.be
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References

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  1. Filippo Altissimo & Antonio Bassanetti & Riccardo Cristadoro & Mario Forni & Marco Lippi & Lucrezia Reichlin & Giovanni Veronese, 2001. "A real time coincident indicator of the euro area business cycle," Temi di discussione (Economic working papers), Bank of Italy, Economic Research and International Relations Area 436, Bank of Italy, Economic Research and International Relations Area.
  2. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2004. "The generalized dynamic factor model consistency and rates," Journal of Econometrics, Elsevier, Elsevier, vol. 119(2), pages 231-255, April.
  3. Mario Forni & Marc Hallin & Lucrezia Reichlin & Marco Lippi, 2000. "The generalised dynamic factor model: identification and estimation," ULB Institutional Repository 2013/10143, ULB -- Universite Libre de Bruxelles.
  4. Chamberlain, Gary & Rothschild, Michael, 1982. "Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets," Scholarly Articles 3230355, Harvard University Department of Economics.
  5. Forni, Mario & Reichlin, Lucrezia, 1998. "Let's Get Real: A Factor Analytical Approach to Disaggregated Business Cycle Dynamics," Review of Economic Studies, Wiley Blackwell, Wiley Blackwell, vol. 65(3), pages 453-73, July.
  6. Forni, Mario, et al, 2001. "Coincident and Leading Indicators for the Euro Area," Economic Journal, Royal Economic Society, Royal Economic Society, vol. 111(471), pages C62-85, May.
  7. Forni, Mario & Lippi, Marco, 2000. "The Generalized Dynamic Factor Model: Representation Theory," CEPR Discussion Papers, C.E.P.R. Discussion Papers 2509, C.E.P.R. Discussion Papers.
  8. James H. Stock & Mark W. Watson, 1998. "Diffusion Indexes," NBER Working Papers 6702, National Bureau of Economic Research, Inc.
  9. Thomas J. Sargent & Christopher A. Sims, 1977. "Business cycle modeling without pretending to have too much a priori economic theory," Working Papers, Federal Reserve Bank of Minneapolis 55, Federal Reserve Bank of Minneapolis.
  10. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, Econometric Society, vol. 70(1), pages 191-221, January.
  11. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 20(2), pages 147-62, April.
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