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A dynamic factor model framework for forecast combination

Author

Listed:
  • 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)

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.

Suggested Citation

  • Yeung Lewis Chan & James H. Stock & Mark W. Watson, 1999. "A dynamic factor model framework for forecast combination," Spanish Economic Review, Springer;Spanish Economic Association, vol. 1(2), pages 91-121.
    • Handle: RePEc:spr:specre:v:1:y:1999:i:2:p:91-121
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    More about this item

    Keywords

    Combination forecasts; principal component regression; James-Stein estimation;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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