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A dynamic factor model framework for forecast combination

Spanish Economic Review volume 1, pages 91–121 (1999)Cite this article

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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Authors and Affiliations

  1. Department of Economics, Harvard University, Cambridge, MA 02138, USA, , , , , , US

    Yeung Lewis Chan

  2. Kennedy School of Government and NBER, 79 John F. Kennedy Street, Harvard University, Cambridge, MA 02138, USA, , , , , , US

    James H. Stock

  3. Woodrow Wilson School, Princeton University, Princeton, NJ 08544, USA, , , , , , US

    Mark W. Watson

Authors
  1. Yeung Lewis Chan
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  2. James H. Stock
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  3. Mark W. Watson
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Chan, Y., Stock, J. & Watson, M. A dynamic factor model framework for forecast combination. Span Econ Rev 1, 91–121 (1999). https://doi.org/10.1007/s101080050005

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  • Issue Date:

  • DOI: https://doi.org/10.1007/s101080050005

  • JEL classification: C32, C22
  • Key words: Combination forecasts, principal component regression, James-Stein estimation