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The forecasting performance of various models for seasonality and nonlinearity for quarterly industrial production

  • Franses, Ph.H.B.F.
  • van Dijk, D.J.C.

Seasonality often accounts for the major part of quarterly or monthly movements in detrended macro-economic time series. In addition, business cycle nonlinearity is a prominent feature of many such series too. A forecaster can nowadays consider a wide variety of time series models which describe seasonal variation and regime-switching behaviour. In this paper we examine the forecasting performance of various models for seasonality and nonlinearity using quarterly industrial production series for 17 OECD countries. We find that forecasting performance varies widely across series, across forecast horizons and across seasons. However, in general, linear models with fairly simple descriptions of seasonality outperform at short forecast horizons, whereas nonlinear models with more elaborate seasonal components dominate at longer horizons.

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Paper provided by Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute in its series Econometric Institute Research Papers with number EI 2001-14.

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Date of creation: 26 Apr 2001
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Handle: RePEc:ems:eureir:1678
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  1. Osborn, Denise R. & Heravi, Saeed & Birchenhall, C. R., 1999. "Seasonal unit roots and forecasts of two-digit European industrial production," International Journal of Forecasting, Elsevier, vol. 15(1), pages 27-47, February.
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  3. Löf, Mårten & Franses, Philip Hans, 2000. "On Forecasting Cointegrated Seasonal Time Series," SSE/EFI Working Paper Series in Economics and Finance 350, Stockholm School of Economics.
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  6. Franses, Ph.H.B.F. & Hoek, H. & Paap, R., 1995. "Bayesian Analysis of Seasonal Unit Roots and Seasonal Mean Shifts," Econometric Institute Research Papers EI 9527-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  7. Lundbergh, Stefan & Teräsvirta, Timo & van Dijk, Dick, 2000. "Time-Varying Smooth Transition Autoregressive Models," SSE/EFI Working Paper Series in Economics and Finance 376, Stockholm School of Economics.
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  10. Canova, F. & Ghysels, E., 1992. "Changes in Seasonal Patters: Are They Cyclical," Cahiers de recherche 9216, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  11. Smith, Jeremy & Otero, Jesus, 1997. "Structural breaks and seasonal integration," Economics Letters, Elsevier, vol. 56(1), pages 13-19, September.
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  13. van Dijk, D.J.C. & Strikholm, B. & Terasvirta, T., 2001. "The effects of institutional and technological change and business cycle fluctiations on seasonal patterns in quarterly industrial production series," Econometric Institute Research Papers EI 2001-12, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
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  17. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
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  23. Paap, Richard & Franses, Philip Hans & Hoek, Henk, 1997. "Mean shifts, unit roots and forecasting seasonal time series," International Journal of Forecasting, Elsevier, vol. 13(3), pages 357-368, September.
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