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Forecasting industrial production with linear, nonlinear, and structural change models

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  • Siliverstovs, B.
  • van Dijk, D.J.C.

Abstract

We compare the forecasting performance of linear autoregressive models, autoregressive models with structural breaks, self-exciting threshold autoregressive models, and Markov switching autoregressive models in terms of point, interval, and density forecasts for h-month growth rates of industrial production of the G7 countries, for the period January 1960-December 2000. The results of point forecast evaluation tests support the established notion in the forecasting literature on the favorable performance of the linear AR model. By contrast, the Markov switching models render more accurate interval and density forecasts than the other models, including the linear AR model. This encouraging finding supports the idea that non-linear models may outperform linear competitors in terms of describing the uncertainty around future realizations of a time series.

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Bibliographic Info

Paper provided by Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute in its series Econometric Institute Research Papers with number EI 2003-16.

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Date of creation: 14 May 2003
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Handle: RePEc:ems:eureir:1716

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Related research

Keywords: density forecasts; forecast evaluation tests; interval forecasts; nonlinearity; structural change;

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References

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  1. van Dijk, D.J.C. & Terasvirta, T. & Franses, Ph.H.B.F., 2000. "Smooth transition autoregressive models - A survey of recent developments," Econometric Institute Research Papers, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute EI 2000-23/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
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Citations

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Cited by:
  1. Manzan, S. & Zerom, D., 2005. "A Multi-Step Forecast Density," CeNDEF Working Papers, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance 05-05, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
  2. Manzan, Sebastiano & Zerom, Dawit, 2008. "A bootstrap-based non-parametric forecast density," International Journal of Forecasting, Elsevier, Elsevier, vol. 24(3), pages 535-550.
  3. Bruno, Giancarlo, 2009. "Non-linear relation between industrial production and business surveys data," MPRA Paper 42337, University Library of Munich, Germany.
  4. Maurício Yoshinori Une & Marcelo Savino Portugal, 2005. "Can fear beat hope? A story of GARCH-in-Mean-Level effects for Emerging Market Country Risks," Econometrics, EconWPA 0509006, EconWPA.
  5. Terasvirta, Timo & van Dijk, Dick & Medeiros, Marcelo C., 2005. "Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination," International Journal of Forecasting, Elsevier, Elsevier, vol. 21(4), pages 755-774.
  6. Houda Ben Hadj Boubaker, 2011. "The Forecasting Performance of Seasonal and Nonlinear Models," Asian Economic and Financial Review, Asian Economic and Social Society, Asian Economic and Social Society, vol. 1(1), pages 26-39, March.
  7. Mehmet Balcilar & Rangan Gupta & Stephen M. Miller, 2012. "The Out-of-Sample Forecasting Performance of Non-Linear Models of Regional Housing Prices in the US," Working papers, University of Connecticut, Department of Economics 2012-12, University of Connecticut, Department of Economics.
  8. Goodness C. Aye & Mehmet Balcilar & Adél Bosch & Rangan Gupta & Francois Stofberg, 2013. "The out-of-sample forecasting performance of non-linear models of real exchange rate behaviour: The case of the South African Rand," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 10(1), pages 121-148, April.
  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, Elsevier, vol. 22(2), pages 341-361.

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