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How Far Can We Forecast? Forecast Content Horizons For Some Important Macroeconomic Time Series

Author

Listed:
  • John G. Galbraith

  • Greg Tkacz

Abstract

For stationary transformations of variables, there exists a maximum horizon beyond which forecasts can provide no more information about the variable than is present in the unconditional mean. Meteorological forecasts, typically excepting only experimental or exploratory situations, are not reported beyond this horizon; by contrast, little generally-accepted information about such maximum horizons is available for economic variables. In this paper we estimate such content horizons for a variety of economic variables, and compare these with the maximum horizons which we observe reported in a large sample of empirical economic forecasting studies. We find that there are many instances of published studies which provide forecasts exceeding, often by substantial margins, our estimates of the content horizon for the particular variable and frequency. We suggest some simple reporting practices for forecasts that could potentially bring greater transparency to the process of making the interpreting economic forecasts.

Suggested Citation

  • John G. Galbraith & Greg Tkacz, 2006. "How Far Can We Forecast? Forecast Content Horizons For Some Important Macroeconomic Time Series," Departmental Working Papers 2006-13, McGill University, Department of Economics.
  • Handle: RePEc:mcl:mclwop:2006-13
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    File URL: http://www.mcgill.ca/files/economics/howfarcan.pdf
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    References listed on IDEAS

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    1. Li Fuchun & Tkacz Greg, 2004. "Combining Forecasts with Nonparametric Kernel Regressions," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 8(4), pages 1-18, December.
    2. Granger, Clive W J, 1996. "Can We Improve the Perceived Quality of Economic Forecasts?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(5), pages 455-473, Sept.-Oct.
    3. Marc Brisson & Bryan Campbell & John W. Galbraith, 2001. "Forecasting Some Low-Predictability Time Series Using Diffusion Indices," CIRANO Working Papers 2001s-46, CIRANO.
    4. Granger, Clive W. J. & Hyung, Namwon, 2004. "Occasional structural breaks and long memory with an application to the S&P 500 absolute stock returns," Journal of Empirical Finance, Elsevier, vol. 11(3), pages 399-421, June.
    5. Galbraith, John W. & KI[#x1e63]Inbay, Turgut, 2005. "Content horizons for conditional variance forecasts," International Journal of Forecasting, Elsevier, vol. 21(2), pages 249-260.
    6. Francis X. Diebold & Lutz Kilian, 2001. "Measuring predictability: theory and macroeconomic applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(6), pages 657-669.
    7. James H. Stock & Mark W. Watson, 1998. "A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series," NBER Working Papers 6607, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Michael K. Andersson & Sune Karlsson, 2008. "Bayesian forecast combination for VAR models," Advances in Econometrics, in: Bayesian Econometrics, pages 501-524, Emerald Group Publishing Limited.
    2. Andersson, Michael K. & Karlsson, Gustav & Svensson, Josef, 2007. "The Riksbank’s Forecasting Performance," Working Paper Series 218, Sveriges Riksbank (Central Bank of Sweden).

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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