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If Nonlinear Models Cannot Forecast, What Use Are They?

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  • Ramsey James B.

    () (New York University)

Abstract

This paper begins with a brief review of the recent experience using nonlinear models and ideas of chaos to model economic data and to provide forecasts that are better than linear models. The record of improvement is at best meager. The remainder of the paper examines some of the reasons for this lack of improvement. The concepts of "openness" and "isolation" are introduced, and a case is made that open and nonisolated systems cannot be forecasted; the extent to which economic systems are closed and isolated provides the true pragmatic limits to forecastability. The reasons why local "overfitting," especially with nonparametric models, leads to worse forecasts are discussed. Models and "representations" of data are distinguished and the reliance on minimum mean-square forecast error to choose between models and representations is evaluated.

Suggested Citation

  • Ramsey James B., 1996. "If Nonlinear Models Cannot Forecast, What Use Are They?," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 1(2), pages 1-24, July.
  • Handle: RePEc:bpj:sndecm:v:1:y:1996:i:2:n:1
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    Cited by:

    1. Amos Golan & Jeffrey M. Perloff, 2004. "Superior Forecasts of the U.S. Unemployment Rate Using a Nonparametric Method," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 433-438, February.
    2. Daniel Buncic, 2012. "Understanding forecast failure of ESTAR models of real exchange rates," Empirical Economics, Springer, vol. 43(1), pages 399-426, August.
    3. Costas Milas & Jesús Otero & Theodore Panagiotidis, 2004. "Forecasting the spot prices of various coffee types using linear and non-linear error correction models," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 9(3), pages 277-288.
    4. Sekioua, Sofiane H., 2006. "Nonlinear adjustment in the forward premium: evidence from a threshold unit root test," International Review of Economics & Finance, Elsevier, vol. 15(2), pages 164-183.
    5. Antonio Aguirre & Luis A. Aguirre, 1998. "Time series analysis of monthly beef cattle prices with non-linear autoregressive models," Textos para Discussão Cedeplar-UFMG td120, Cedeplar, Universidade Federal de Minas Gerais.
    6. Terasvirta, Timo, 2006. "Forecasting economic variables with nonlinear models," Handbook of Economic Forecasting, Elsevier.
    7. Simpson, Paul W & Osborn, Denise R & Sensier, Marianne, 2001. "Forecasting UK Industrial Production over the Business Cycle," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(6), pages 405-424, September.
    8. Ilias Lekkos & Costas Milas & Theodore Panagiotidis, 2007. "Forecasting interest rate swap spreads using domestic and international risk factors: evidence from linear and non-linear models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(8), pages 601-619.
    9. Rapach, David E. & Wohar, Mark E., 2006. "The out-of-sample forecasting performance of nonlinear models of real exchange rate behavior," International Journal of Forecasting, Elsevier, vol. 22(2), pages 341-361.
    10. repec:ebl:ecbull:v:7:y:2005:i:1:p:1-6 is not listed on IDEAS

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