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A Simple Recursive Forecasting Model

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  • 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.

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Bibliographic Info

Paper provided by University of Oregon Economics Department in its series University of Oregon Economics Department Working Papers with number 2005-3.

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Length: 10
Date of creation: 01 Feb 2005
Date of revision: 01 Feb 2005
Handle: RePEc:ore:uoecwp:2005-3

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Keywords: constant gain; recursive learning; expectations;

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References

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  1. Milani, Fabio, 2007. "Expectations, learning and macroeconomic persistence," Journal of Monetary Economics, Elsevier, vol. 54(7), pages 2065-2082, October.
  2. Andrew C. Harvey, 1990. "The Econometric Analysis of Time Series, 2nd Edition," MIT Press Books, The MIT Press, edition 2, volume 1, number 026208189x, December.
  3. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1986. "Forecasting and conditional projection using realistic prior distribution," Staff Report 93, Federal Reserve Bank of Minneapolis.
  4. Tom Stark & Dean Croushore, 2001. "Forecasting with a real-time data set for macroeconomists," Working Papers 01-10, Federal Reserve Bank of Philadelphia.
  5. James H. Stock & Mark W. Watson, 1994. "Evidence on structural instability in macroeconomic times series relations," Working Paper Series, Macroeconomic Issues 94-13, Federal Reserve Bank of Chicago.
  6. 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.
  7. Pesaran, M Hashem & Timmermann, Allan, 1995. " Predictability of Stock Returns: Robustness and Economic Significance," Journal of Finance, American Finance Association, vol. 50(4), pages 1201-28, September.
  8. In-Koo Cho & Kenneth Kasa, 2003. "Learning Dynamics and Endogenous Currency Crises," Computing in Economics and Finance 2003 132, Society for Computational Economics.
  9. Wiliam Branch & George W. Evans, 2005. "Model Uncertainty and Endogenous Volatility," University of Oregon Economics Department Working Papers 2005-21, University of Oregon Economics Department, revised 26 Oct 2006.
  10. Evans, George W. & Honkapohja , Seppo, 2003. "Policy interaction, expectations and the liquidity trap," Research Discussion Papers 22/2003, Bank of Finland.
  11. James B. Bullard & John Duffy, 2004. "Learning and structural change in macroeconomic data," Working Papers 2004-016, Federal Reserve Bank of St. Louis.
  12. Kenneth Kasa, 2004. "Learning, Large Deviations, And Recurrent Currency Crises," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 45(1), pages 141-173, 02.
  13. 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.
  14. William Poole, 2002. "Flation," Speech 49, Federal Reserve Bank of St. Louis.
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