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A dynamic factor model framework for forecast combination

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Author Info
Yeung Lewis Chan (Department of Economics, Harvard University, Cambridge, MA 02138, USA Kennedy School of Government and NBER, 79 John F. Kennedy Street, Harvard University, Cambridge, MA 02138, USA Woodrow Wilson School, Princeton University, Princeton, NJ 08544, USA)
James H. Stock (Department of Economics, Harvard University, Cambridge, MA 02138, USA Kennedy School of Government and NBER, 79 John F. Kennedy Street, Harvard University, Cambridge, MA 02138, USA Woodrow Wilson School, Princeton University, Princeton, NJ 08544, USA)
Mark W. Watson (Department of Economics, Harvard University, Cambridge, MA 02138, USA Kennedy School of Government and NBER, 79 John F. Kennedy Street, Harvard University, Cambridge, MA 02138, USA Woodrow Wilson School, Princeton University, Princeton, NJ 08544, USA)

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Abstract

A panel of ex-ante forecasts of a single time series is modeled as a dynamic factor model, where the conditional expectation is the single unobserved factor. When applied to out-of-sample forecasting, this leads to combination forecasts that are based on methods other than OLS. These methods perform well in a Monte Carlo experiment. These methods are evaluated empirically in a panel of simulated real-time computer-generated univariate forecasts of U.S. macroeconomic time series.

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Publisher Info
Article provided by Springer in its journal Spanish Economic Review.

Volume (Year): 1 (1999)
Issue (Month): 2 ()
Pages: 91-121
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Handle: RePEc:spr:specre:v:1:y:1999:i:2:p:91-121

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Related research
Keywords: Combination forecasts principal component regression James-Stein estimation

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Find related papers by JEL classification:
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models

Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Timmermann, Allan G, 2005. "Forecast Combinations," CEPR Discussion Papers 5361, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
  2. Aiolfi, Marco & Favero, Carlo A, 2003. "Model Uncertainty, Thick Modelling and the Predictability of Stock Returns," CEPR Discussion Papers 3997, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
    Other versions:
  3. David E. Rapach & Jack K. Strauss, 2005. "Forecasting employment growth in Missouri with many potentially relevant predictors: an analysis of forecast combining methods," Regional Economic Development, Federal Reserve Bank of St. Louis, issue Nov, pages 97-112. [Downloadable!]
  4. Pilar Poncela & Eva Senra, 2006. "A two factor model to combine US inflation forecasts," Applied Economics, Taylor and Francis Journals, vol. 38(18), pages 2191-2197, October. [Downloadable!] (restricted)
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