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A simple recursive forecasting model

  • Branch, William A.
  • Evans, George W.

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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Article provided by Elsevier in its journal Economics Letters.

Volume (Year): 91 (2006)
Issue (Month): 2 (May)
Pages: 158-166

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Handle: RePEc:eee:ecolet:v:91:y:2006:i:2:p:158-166
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  8. 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.
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  10. Cho, In-Koo & Williams, Noah & Sargent, Thomas J, 2002. "Escaping Nash Inflation," Review of Economic Studies, Wiley Blackwell, vol. 69(1), pages 1-40, January.
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  12. 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.
  13. Cho, In-Koo & Kasa, Kenneth, 2008. "Learning Dynamics And Endogenous Currency Crises," Macroeconomic Dynamics, Cambridge University Press, vol. 12(02), pages 257-285, April.
  14. Andrew C. Harvey, 1990. "The Econometric Analysis of Time Series, 2nd Edition," MIT Press Books, The MIT Press, edition 2, volume 1, number 026208189x, June.
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