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A Simple Recursive Forecasting Model

Author

Listed:
  • Wiliam Branch

    (University of California - Irvine)

  • George W. Evans

    () (University of Oregon Economics Department)

Abstract

We compare the performance of alternative recursive forecasting models. A simple constant gain algorithm, used widely in the learning literature, both forecasts well out of sample and also provides the best fit to the Survey of Professional Forecasters.

Suggested Citation

  • Wiliam Branch & George W. Evans, 2005. "A Simple Recursive Forecasting Model," University of Oregon Economics Department Working Papers 2005-3, University of Oregon Economics Department, revised 01 Feb 2005.
  • Handle: RePEc:ore:uoecwp:2005-3
    as

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    File URL: http://economics.uoregon.edu/papers/UO-2005-3_Evans_Simple_Forecasting.pdf
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    References listed on IDEAS

    as
    1. William Branch & George W. Evans, 2007. "Model Uncertainty and Endogenous Volatility," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 10(2), pages 207-237, April.
    2. George W. Evans & Seppo Honkapohja, 2005. "Policy Interaction, Expectations and the Liquidity Trap," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 303-323, April.
    3. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1983. "Forecasting and Conditional Projection Using Realistic Prior Distributions," NBER Working Papers 1202, National Bureau of Economic Research, Inc.
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    5. William Poole & Robert H. Rasche, 2002. "Flation," Review, Federal Reserve Bank of St. Louis, issue Nov, pages 1-6.
      • William Poole, 2002. "Flation," Speech 49, Federal Reserve Bank of St. Louis.
    6. Cho, In-Koo & Kasa, Kenneth, 2008. "Learning Dynamics And Endogenous Currency Crises," Macroeconomic Dynamics, Cambridge University Press, pages 257-285.
    7. Stark, Tom & Croushore, Dean, 2002. "Forecasting with a real-time data set for macroeconomists," Journal of Macroeconomics, Elsevier, pages 507-531.
    8. In-Koo Cho & Noah Williams & Thomas J. Sargent, 2002. "Escaping Nash Inflation," Review of Economic Studies, Oxford University Press, vol. 69(1), pages 1-40.
    9. Andrew C. Harvey, 1990. "The Econometric Analysis of Time Series, 2nd Edition," MIT Press Books, The MIT Press, edition 2, volume 1, number 026208189x, January.
    10. Stark, Tom & Croushore, Dean, 2002. "Reply to the comments on 'Forecasting with a real-time data set for macroeconomists'," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 563-567, December.
    11. Milani, Fabio, 2007. "Expectations, learning and macroeconomic persistence," Journal of Monetary Economics, Elsevier, vol. 54(7), pages 2065-2082, October.
    12. Pesaran, M Hashem & Timmermann, Allan, 1995. " Predictability of Stock Returns: Robustness and Economic Significance," Journal of Finance, American Finance Association, vol. 50(4), pages 1201-1228, September.
    13. Stock, James H & Watson, Mark W, 1996. "Evidence on Structural Instability in Macroeconomic Time Series Relations," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 11-30, January.
    14. James B. Bullard & John Duffy, 2004. "Learning and structural change in macroeconomic data," Working Papers 2004-016, Federal Reserve Bank of St. Louis.
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    More about this item

    Keywords

    constant gain; recursive learning; expectations;

    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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