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The Forecasting Performance of Various Models for Seasonality and Nonlinearity for Quarterly Industrial Production

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Author Info
P.H. Franses ()
D. Van Dijk () (FEW-Econometrie en besliskunde)

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Abstract

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, Econometric Institute in its series Econometric Institute Report with number 222.

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Date of creation: 2001
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Handle: RePEc:dgr:eureir:2001222

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Related research
Keywords: forecasting nonlinearity seasonality industrial production;

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Philip Hans Franses & Timothy J. Vogelsang, 1998. "On Seasonal Cycles, Unit Roots, And Mean Shifts," The Review of Economics and Statistics, MIT Press, vol. 80(2), pages 231-240, May. [Downloadable!] (restricted)
  2. Clements, Michael P & Smith, Jeremy, 1999. "A Monte Carlo Study of the Forecasting Performance of Empirical SETAR Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(2), pages 123-41, March-Apr. [Downloadable!]
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  3. 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. [Downloadable!] (restricted)
  4. Richard Paap & Philip Hans Franses, 1999. "On trends and constants in periodic autoregressions," Econometric Reviews, Taylor and Francis Journals, vol. 18(3), pages 271-286. [Downloadable!] (restricted)
  5. Ph.H.B.F. Franses & R. Paap, 1999. "Forecasting with periodic autoregressive time series models," Econometric Institute Report 156, Erasmus University Rotterdam, Econometric Institute. [Downloadable!]
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  6. Lundbergh, Stefan & Terasvirta, Timo & van Dijk, Dick, 2003. "Time-Varying Smooth Transition Autoregressive Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 104-21, January.
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  7. Hylleberg, S. & Engle, R. F. & Granger, C. W. J. & Yoo, B. S., 1990. "Seasonal integration and cointegration," Journal of Econometrics, Elsevier, vol. 44(1-2), pages 215-238. [Downloadable!] (restricted)
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  8. Canova, Fabio & Ghysels, Eric, 1994. "Changes in seasonal patterns : Are they cyclical?," Journal of Economic Dynamics and Control, Elsevier, vol. 18(6), pages 1143-1171, November. [Downloadable!] (restricted)
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  9. Franses, Philip Hans & Hoek, Henk & Paap, Richard, 1997. "Bayesian analysis of seasonal unit roots and seasonal mean shifts," Journal of Econometrics, Elsevier, vol. 78(2), pages 359-380, June. [Downloadable!] (restricted)
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  10. Hylleberg, Svend & Jorgensen, Clara & Sorensen, Nils Karl, 1993. "Seasonality in Macroeconomic Time Series," Empirical Economics, Springer, vol. 18(2), pages 321-35.
  11. Terasvirta, T & Anderson, H M, 1992. "Characterizing Nonlinearities in Business Cycles Using Smooth Transition Autoregressive Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages S119-36, Suppl. De. [Downloadable!] (restricted)
  12. Canova, Fabio & Hansen, Bruce E, 1995. "Are Seasonal Patterns Constant over Time? A Test for Seasonal Stability," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 237-52, July.
  13. Osborn, Denise R & Smith, Jeremy P, 1989. "The Performance of Periodic Autoregressive Models in Forecasting Seasonal U. K. Consumption," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(1), pages 117-27, January.
  14. Clements, Michael P. & Hendry, David F., 1997. "An empirical study of seasonal unit roots in forecasting," International Journal of Forecasting, Elsevier, vol. 13(3), pages 341-355, September. [Downloadable!] (restricted)
  15. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 254-59, April.
  16. Smith, Jeremy & Otero, Jesus, 1997. "Structural breaks and seasonal integration," Economics Letters, Elsevier, vol. 56(1), pages 13-19, September. [Downloadable!] (restricted)
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  17. Ghysels, E., 1990. "On the Economic and Econometrics of Seasonality," Cahiers de recherche 9028, Universite de Montreal, Departement de sciences economiques.
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  18. Skalin, Joakim & Teräsvirta, Timo, 1998. "Modelling asymmetries and moving equilibria in unemployment rates," Working Paper Series in Economics and Finance 262, Stockholm School of Economics, revised 05 Oct 1998.
  19. Lof, Marten & Hans Franses, Philip, 2001. "On forecasting cointegrated seasonal time series," International Journal of Forecasting, Elsevier, vol. 17(4), pages 607-621. [Downloadable!] (restricted)
  20. Osborn, Denise R, 1988. "Seasonality and Habit Persistence in a Life Cycle Model of Consumptio n," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 3(4), pages 255-66, October-D. [Downloadable!] (restricted)
  21. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
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  22. Potter, Simon M, 1995. "A Nonlinear Approach to US GNP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(2), pages 109-25, April-Jun. [Downloadable!] (restricted)
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  23. Löf, Mårten & Franses, Philip Hans, 2000. "On Forecasting Cointegrated Seasonal Time Series," Working Paper Series in Economics and Finance 350, Stockholm School of Economics. [Downloadable!]
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Full references

Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. John W. Galbraith & Greg Tkacz, 2007. "How Far Can Forecasting Models Forecast? Forecast Content Horizons for Some Important Macroeconomic Variables," Working Papers 07-1, Bank of Canada. [Downloadable!]
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