IDEAS home Printed from https://ideas.repec.org/a/eee/jomega/v26y1998i6p751-767.html
   My bibliography  Save this article

Forecasting industrial production using structural time series models

Author

Listed:
  • Thury, Gerhard
  • Witt, Stephen F.

Abstract

Industrial production data series are volatile and often also cyclical. Hence, univariate time series models which allow for these features are expected to generate relatively accurate forecasts of industrial production. A particular class of unobservable components models -- structural time series models -- is used to generate forecasts of Austrian and German industrial production. A widely applied ARIMA model is used as a baseline for comparison. The empirical results show that the basic structural model generates more accurate forecasts than the ARIMA model when accuracy is measured in terms of size of error or directional change; and that the basic structural model forecasts better than the structural model with a cyclical component included on the basis of numerical measures, and tracking error for month-to-month changes.

Suggested Citation

  • Thury, Gerhard & Witt, Stephen F., 1998. "Forecasting industrial production using structural time series models," Omega, Elsevier, vol. 26(6), pages 751-767, December.
  • Handle: RePEc:eee:jomega:v:26:y:1998:i:6:p:751-767
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0305-0483(98)00024-3
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gersch, Will & Kitagawa, Genshiro, 1983. "The Prediction of Time Series with Trends and Seasonalities," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(3), pages 253-264, July.
    2. Sims, Cristopher A, 1985. "Comment on "Issues Involved with the Seasonal Adjustment of Economic Time Series."," Journal of Business & Economic Statistics, American Statistical Association, vol. 3(1), pages 92-94, January.
    3. Makridakis, Spyros, 1986. "The art and science of forecasting An assessment and future directions," International Journal of Forecasting, Elsevier, vol. 2(1), pages 15-39.
    4. Bell, William R & Hillmer, Steven C, 1984. "Issues Involved with the Seasonal Adjustment of Economic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 291-320, October.
    5. Winklhofer, Heidi & Diamantopoulos, Adamantios & Witt, Stephen F., 1996. "Forecasting practice: A review of the empirical literature and an agenda for future research," International Journal of Forecasting, Elsevier, vol. 12(2), pages 193-221, June.
    6. Bodo, Giorgio & Signorini, Luigi Federico, 1987. "Short-term forecasting of the industrial production index," International Journal of Forecasting, Elsevier, vol. 3(2), pages 245-259.
    7. Martin, Christine A. & Witt, Stephen F., 1989. "Forecasting tourism demand: A comparison of the accuracy of several quantitative methods," International Journal of Forecasting, Elsevier, vol. 5(1), pages 7-19.
    8. Harvey, A C & Todd, P H J, 1983. "Forecasting Economic Time Series with Structural and Box-Jenkins Models: A Case Study," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(4), pages 299-307, October.
    9. Harvey, A C & Todd, P H J, 1983. "Forecasting Economic Time Series with Structural and Box-Jenkins Models: A Case Study: Response," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(4), pages 313-315, October.
    10. Liu, Lon-Mu, 1986. "Identification of time series models in the presence of calendar variation," International Journal of Forecasting, Elsevier, vol. 2(3), pages 357-372.
    11. Cicarelli, James, 1982. "A new method of evaluating the accuracy of economic forecasts," Journal of Macroeconomics, Elsevier, vol. 4(4), pages 469-475.
    12. Makridakis, Spyros & Chatfield, Chris & Hibon, Michele & Lawrence, Michael & Mills, Terence & Ord, Keith & Simmons, LeRoy F., 1993. "The M2-competition: A real-time judgmentally based forecasting study," International Journal of Forecasting, Elsevier, vol. 9(1), pages 5-22, April.
    13. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
    14. Nerlove, Marc & Grether, David M. & Carvalho, José L., 1979. "Analysis of Economic Time Series," Elsevier Monographs, Elsevier, edition 1, number 9780125157506 edited by Shell, Karl.
    15. Robert F. Engle, 1978. "Estimating Structural Models of Seasonality," NBER Chapters, in: Seasonal Analysis of Economic Time Series, pages 281-308, National Bureau of Economic Research, Inc.
    16. Bell, William R & Hillmer, Steven C, 1984. "Issues Involved with the Seasonal Adjustment of Time Series: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 343-349, October.
    17. Price, D. H. R. & Sharp, J. A., 1986. "A comparison of the performance of different univariate forecasting methods in a model of capacity acquisition in UK electricity supply," International Journal of Forecasting, Elsevier, vol. 2(3), pages 333-348.
    18. Andrews, Rick L, 1994. "Forecasting Performance of Structural Time Series Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(1), pages 129-133, January.
    19. Funke, Michael, 1990. "Assessing the forecasting accuracy of monthly vector autoregressive models : The case of five OECD countries," International Journal of Forecasting, Elsevier, vol. 6(3), pages 363-378, October.
    20. Lawrence, Michael J. & Edmundson, Robert H. & O'Connor, Marcus J., 1985. "An examination of the accuracy of judgmental extrapolation of time series," International Journal of Forecasting, Elsevier, vol. 1(1), pages 25-35.
    21. George E. P. Box & Steven C. Hillmer & George C. Tiao, 1978. "Analysis and Modeling of Seasonal Time Series," NBER Chapters, in: Seasonal Analysis of Economic Time Series, pages 309-344, National Bureau of Economic Research, Inc.
    22. Fildes, Robert, 1992. "The evaluation of extrapolative forecasting methods," International Journal of Forecasting, Elsevier, vol. 8(1), pages 81-98, June.
    23. M.R. Grupe & W.P. Cleveland, 1981. "Modeling time series when calendar effects are present," Special Studies Papers 162, Board of Governors of the Federal Reserve System (U.S.).
    24. Boucelham, Jamel & Terasvirta, Timo, 1990. "Use of preliminary values in forecasting industrial production," International Journal of Forecasting, Elsevier, vol. 6(4), pages 463-468, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mohammadipour, Maryam & Boylan, John E., 2012. "Forecast horizon aggregation in integer autoregressive moving average (INARMA) models," Omega, Elsevier, vol. 40(6), pages 703-712.
    2. 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.
    3. Zhao, Weigang & Wang, Jianzhou & Lu, Haiyan, 2014. "Combining forecasts of electricity consumption in China with time-varying weights updated by a high-order Markov chain model," Omega, Elsevier, vol. 45(C), pages 80-91.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jomega:v:26:y:1998:i:6:p:751-767. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Haili He). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/375/description#description .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.