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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
    DOI: 10.2202/1558-3708.1013
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    Cited by:

    1. 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.
    2. 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.
    3. Claudio Bonilla & Rafael Romero-Meza & Melvin Hinich, 2006. "Episodic nonlinearity in Latin American stock market indices," Applied Economics Letters, Taylor & Francis Journals, vol. 13(3), pages 195-199.
    4. 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.
    5. Xiaojie Xu, 2017. "Short-run price forecast performance of individual and composite models for 496 corn cash markets," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(14), pages 2593-2620, October.
    6. 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.
    7. Daniel Buncic, 2012. "Understanding forecast failure of ESTAR models of real exchange rates," Empirical Economics, Springer, vol. 43(1), pages 399-426, August.
    8. Kian-Ping Lim & Melvin J. Hinich & Venus Khim-Sen Liew, 2005. "Statistical Inadequacy of GARCH Models for Asian Stock Markets," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 4(3), pages 263-279, December.
    9. Siliverstovs, B. & van Dijk, D.J.C., 2003. "Forecasting industrial production with linear, nonlinear, and structural change models," Econometric Institute Research Papers EI 2003-16, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    10. 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.
    11. Swanson, Norman R. & White, Halbert, 1997. "Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models," International Journal of Forecasting, Elsevier, vol. 13(4), pages 439-461, December.
    12. Ramsey, J.B., 2002. "Wavelets in Economics and Finance: Past and Future," Working Papers 02-02, C.V. Starr Center for Applied Economics, New York University.
    13. repec:ebl:ecbull:v:7:y:2005:i:1:p:1-6 is not listed on IDEAS
    14. 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.
    15. Dick van Dijk & Philip Hans Franses, 2003. "Selecting a Nonlinear Time Series Model using Weighted Tests of Equal Forecast Accuracy," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 727-744, December.
    16. Agnon, Yehuda & Golan, Amos & Shearer, Matthew, 1999. "Nonparametric, nonlinear, short-term forecasting: theory and evidence for nonlinearities in the commodity markets," Economics Letters, Elsevier, vol. 65(3), pages 293-299, December.
    17. Ramsey James B., 2002. "Wavelets in Economics and Finance: Past and Future," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 6(3), pages 1-29, November.
    18. Kian-Ping Lim & Melvin J. Hinich, 2005. "Cross-temporal universality of non-linear dependencies in Asian stock markets," Economics Bulletin, AccessEcon, vol. 7(1), pages 1-6.
    19. Xiaojie Xu, 2020. "Corn Cash Price Forecasting," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(4), pages 1297-1320, August.
    20. 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.
    21. Terasvirta, Timo, 2006. "Forecasting economic variables with nonlinear models," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 8, pages 413-457, Elsevier.
    22. Bampinas Georgios & Panagiotidis Theodore, 2015. "On the relationship between oil and gold before and after financial crisis: linear, nonlinear and time-varying causality testing," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(5), pages 657-668, December.

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