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The Generalized Dynamic Factor Model. One-Sided Estimation and Forecasting

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Author Info
Mario Forni
Marc Hallin
Marco Lippi
Lucrezia Reichlin

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Abstract

This paper proposes a new forecasting method that exploits information from a largepanel of time series. The method is based on the generalized dynamic factor model proposedin Forni, Hallin, Lippi, and Reichlin (2000), and takes advantage of the information onthe dynamic covariance structure of the whole panel. We first use our previous method toobtain 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 consistentestimate of the optimal forecast. This two-step approach solves the end-of-sample problemscaused by two-sided filtering (as in our previous work), while retaining the advantages of anestimator based on dynamic information. The relative merits of our method and the oneproposed by Stock and Watson (2002) are discussed.

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Paper provided by Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy in its series LEM Papers Series with number 2003/13.

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Date of creation: 15 Dec 2003
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Handle: RePEc:ssa:lemwps:2003/13

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Related research
Keywords: Dynamic factor models principal components time series large cross-sections panel data forecasting.

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  1. Forni, Mario & Reichlin, Lucrezia, 1998. "Let's Get Real: A Factor Analytical Approach to Disaggregated Business Cycle Dynamics," Review of Economic Studies, Blackwell Publishing, vol. 65(3), pages 453-73, July. [Downloadable!] (restricted)
  2. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2004. "The generalized dynamic factor model consistency and rates," Journal of Econometrics, Elsevier, vol. 119(2), pages 231-255, April. [Downloadable!] (restricted)
  3. James H. Stock & Mark W. Watson, 1998. "Diffusion Indexes," NBER Working Papers 6702, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
  4. Forni, Mario, et al, 2001. "Coincident and Leading Indicators for the Euro Area," Economic Journal, Royal Economic Society, vol. 111(471), pages C62-85, May. [Downloadable!] (restricted)
  5. Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Boston College Working Papers in Economics 440, Boston College Department of Economics. [Downloadable!]
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  6. Forni, Mario & Lippi, Marco, 2001. "The Generalized Dynamic Factor Model: Representation Theory," Econometric Theory, Cambridge University Press, vol. 17(06), pages 1113-1141, December. [Downloadable!]
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  7. Thomas J. Sargent & Christopher A. Sims, 1977. "Business cycle modeling without pretending to have too much a priori economic theory," Working Papers 55, Federal Reserve Bank of Minneapolis. [Downloadable!]
  8. 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) 436, Bank of Italy, Economic Research Department. [Downloadable!]
  9. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
  10. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 1999. "The Generalized Dynamic Factor Model: Identification and Estimation," CEPR Discussion Papers 2338, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
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  11. D Quah & T Sargent, 1993. "A Dynamic Index Model for Large Cross Sections," CEP Discussion Papers 0132, Centre for Economic Performance, LSE.
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