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

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  • Franses, Philip Hans
  • van Dijk, Dick

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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  • Franses, Philip Hans & van Dijk, Dick, 2005. "The forecasting performance of various models for seasonality and nonlinearity for quarterly industrial production," International Journal of Forecasting, Elsevier, vol. 21(1), pages 87-102.
  • Handle: RePEc:eee:intfor:v:21:y:2005:i:1:p:87-102
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    2. Xiao, Yi & Liu, John J. & Hu, Yi & Wang, Yingfeng & Lai, Kin Keung & Wang, Shouyang, 2014. "A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting," Journal of Air Transport Management, Elsevier, pages 1-11.
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    9. Andrea BASTIANIN & Marzio GALEOTTI & Matteo MANERA, 2011. "Forecast evaluation in call centers: combined forecasts, flexible loss functions and economic criteria," Departmental Working Papers 2011-08, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    10. Beckmann, Joscha, 2013. "Nonlinear adjustment, purchasing power parity and the role of nominal exchange rates and prices," The North American Journal of Economics and Finance, Elsevier, vol. 24(C), pages 176-190.
    11. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    12. Andrea Bastianin & Marzio Galeotti & Matteo Manera, 2011. "Forecast Evaluation in Call Centers: Combined Forecasts, Flexible Loss Functions and Economic Criteria," Working Papers 20110301, Università degli Studi di Milano-Bicocca, Dipartimento di Statistica.
    13. Franses,Philip Hans & Dijk,Dick van & Opschoor,Anne, 2014. "Time Series Models for Business and Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521520911, December.
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    15. Joscha Beckmann, 2011. "Nonlinear Adjustment, Purchasing Power Parity and the Role of Nominal Exchange Rates and Prices," Ruhr Economic Papers 0272, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    16. Bruno, Giancarlo, 2009. "Non-linear relation between industrial production and business surveys data," MPRA Paper 42337, University Library of Munich, Germany.
    17. Charles S. Bos & Siem Jan Koopman, 2010. "Models with Time-varying Mean and Variance: A Robust Analysis of U.S. Industrial Production," Tinbergen Institute Discussion Papers 10-017/4, Tinbergen Institute.
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    20. Donya Rahmani & Saeed Heravi & Hossein Hassani & Mansi Ghodsi, 2016. "Forecasting time series with structural breaks with Singular Spectrum Analysis, using a general form of recurrent formula," Papers 1605.02188, arXiv.org.
    21. John W. Galbraith & Greg Tkacz, 2007. "How Far Can Forecasting Models Forecast? Forecast Content Horizons for Some Important Macroeconomic Variables," Staff Working Papers 07-1, Bank of Canada.
    22. Jan G. de Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Tinbergen Institute Discussion Papers 05-068/4, Tinbergen Institute.

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