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Do seasonal unit roots matter for forecasting monthly industrial production?

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  • Philip Hans Franses

    (Econometric Institute, Erasmus University Rotterdam, The Netherlands)

  • Yoshinori Kawasaki

    (The Institute of Statistical Mathematics, Tokyo, Japan)

Abstract

We investigate the seasonal unit root properties of monthly industrial production series for 16 OECD countries within the context of a structural time series model. A basic version of this model assumes that there are 11 such seasonal unit roots. We propose to use model selection criteria (AIC and BIC) to examine if one or more of these are in fact stationary. We generally find that when these criteria indicate that a smaller number of seasonal unit roots can be assumed and hence that some seasonal roots are stationary, the corresponding model also gives more accurate one-step-ahead forecasts. Copyright © 2004 John Wiley & Sons, Ltd.

Suggested Citation

  • Philip Hans Franses & Yoshinori Kawasaki, 2004. "Do seasonal unit roots matter for forecasting monthly industrial production?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(2), pages 77-88.
    • Handle: RePEc:jof:jforec:v:23:y:2004:i:2:p:77-88
    DOI: 10.1002/for.901
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    References listed on IDEAS

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    1. Maravall, Agustin, 1985. "On Structural Time Series Models and the Characterization of Components," Journal of Business & Economic Statistics, American Statistical Association, vol. 3(4), pages 350-355, October.
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    3. [Reference to Proietti], Tommaso, 2000. "Comparing seasonal components for structural time series models," International Journal of Forecasting, Elsevier, vol. 16(2), pages 247-260.
    4. Harvey, A C, 1985. "Trends and Cycles in Macroeconomic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 3(3), pages 216-227, June.
    5. Franses, Philip Hans, 1996. "Recent Advances in Modelling Seasonality," Journal of Economic Surveys, Wiley Blackwell, vol. 10(3), pages 299-345, September.
    6. Genshiro Kitagawa, 1981. "A Nonstationary Time Series Model And Its Fitting By A Recursive Filter," Journal of Time Series Analysis, Wiley Blackwell, vol. 2(2), pages 103-116, March.
    7. Yoshinori Kawasaki & Philip Hans Franses, 2003. "Detecting seasonal unit roots in a structural time series model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(4), pages 373-387.
    8. 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.
    9. Thury, Gerhard & Witt, Stephen F., 1998. "Forecasting industrial production using structural time series models," Omega, Elsevier, vol. 26(6), pages 751-767, December.
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    Cited by:

    1. Richard Kleijn & Herman K. van Dijk, 2006. "Bayes model averaging of cyclical decompositions in economic time series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(2), pages 191-212.
    2. Lemmens, Aurélie & Croux, Christophe & Dekimpe, Marnik G., 2008. "Measuring and testing Granger causality over the spectrum: An application to European production expectation surveys," International Journal of Forecasting, Elsevier, vol. 24(3), pages 414-431.
    3. John 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.
    4. Garcia-Ferrer, A. & de Juan, A. & Poncela, P., 2006. "Forecasting traffic accidents using disaggregated data," International Journal of Forecasting, Elsevier, vol. 22(2), pages 203-222.
    5. 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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