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Performance of Alternative Forecasting Methods for Setar Models

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  • Clements, Michael P
  • Smith, Jeremy

Abstract

Five alternative forecasting methods used for SETAR modeling are compared with each other, and relative to mis-specified linear AR models, using Monte Carlo simulation. The results show that for forecasting beyond 1-step ahead, the method that uses Monte Carlo to generate forecasts out-perform the other five methods, when the SETAR model is assumed known. However, with parameter uncertainty the bootstrap method sometimes dominates the Monte Carlo method. The alternative forecasting methods are then used to generate multi-period forecasts of US GNP from the SETAR models, and these forecasts are compared to those from linear models. our results highlight the need for the forecast period to contain 'nonlinear features' if the nonlinear model is to out-perform the simpler linear model

Suggested Citation

  • Clements, Michael P & Smith, Jeremy, 1996. "Performance of Alternative Forecasting Methods for Setar Models," The Warwick Economics Research Paper Series (TWERPS) 467, University of Warwick, Department of Economics.
  • Handle: RePEc:wrk:warwec:467
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    File URL: https://www2.warwick.ac.uk/fac/soc/economics/research/workingpapers/1995-1998/twerp_467.pdf
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    References listed on IDEAS

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    1. Potter, Simon M, 1995. "A Nonlinear Approach to US GNP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(2), pages 109-125, April-Jun.
    2. De Gooijer, Jan G. & Kumar, Kuldeep, 1992. "Some recent developments in non-linear time series modelling, testing, and forecasting," International Journal of Forecasting, Elsevier, vol. 8(2), pages 135-156, October.
    3. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, pages 821-856.
    4. Hansen, Bruce E, 1996. "Inference When a Nuisance Parameter Is Not Identified under the Null Hypothesis," Econometrica, Econometric Society, vol. 64(2), pages 413-430, March.
    5. Clements, Michael P & Hendry, David F, 1996. "Multi-step Estimation for Forecasting," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 58(4), pages 657-684, November.
    6. Diebold, Francis X. & Nason, James A., 1990. "Nonparametric exchange rate prediction?," Journal of International Economics, Elsevier, vol. 28(3-4), pages 315-332, May.
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    Cited by:

    1. Dick van Dijk & Timo Terasvirta & Philip Hans Franses, 2002. "Smooth Transition Autoregressive Models — A Survey Of Recent Developments," Econometric Reviews, Taylor & Francis Journals, vol. 21(1), pages 1-47.
    2. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2012. "Was the Recent Downturn in US GDP Predictable?," Working Papers 201230, University of Pretoria, Department of Economics.

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