IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v7y1991i2p199-208.html
   My bibliography  Save this article

Seasonality, non-stationarity and the forecasting of monthly time series

Author

Listed:
  • Franses, Philip Hans

Abstract

In this paper the focus is on two forecasting models for a monthly time series. The first model requires that the variable is first order and seasonally differenced. The second model considers the series only in its first order differences, while seasonality is modeled with a constant and seasonal dummies. A method to empirically distinguish between these two models is presented. The relevance of this method is established by simulation results, as well as empirical evidence, which show that,. firstly, conventional autocorrelation checks are often not discriminative, and, secondly, that considering the first model while the second is more appropriate yields a deterioration of forecasting performance.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Franses, Philip Hans, 1991. "Seasonality, non-stationarity and the forecasting of monthly time series," International Journal of Forecasting, Elsevier, vol. 7(2), pages 199-208, August.
    • Handle: RePEc:eee:intfor:v:7:y:1991:i:2:p:199-208
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/0169-2070(91)90054-Y
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

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

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Osborn, Denise R., 1990. "A survey of seasonality in UK macroeconomic variables," International Journal of Forecasting, Elsevier, vol. 6(3), pages 327-336, October.
    4. Granger, C. W. J. & Newbold, Paul, 1986. "Forecasting Economic Time Series," Elsevier Monographs, Elsevier, edition 2, number 9780122951831 edited by Shell, Karl.
    5. Heuts, R. M. J. & Bronckers, J. H. J. M., 1988. "Forecasting the Dutch heavy truck market : A multivariate approach," International Journal of Forecasting, Elsevier, vol. 4(1), pages 57-79.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Joseph Beaulieu, J. & Miron, Jeffrey A., 1993. "Seasonal unit roots in aggregate U.S. data," Journal of Econometrics, Elsevier, vol. 55(1-2), pages 305-328.
    2. Franses, P. H., 1990. "Seasonality, Outliers And Linearity," Econometric Institute Archives 272395, Erasmus University Rotterdam.
    3. LeBaron, Blake, 2003. "Non-Linear Time Series Models in Empirical Finance,: Philip Hans Franses and Dick van Dijk, Cambridge University Press, Cambridge, 2000, 296 pp., Paperback, ISBN 0-521-77965-0, $33, [UK pound]22.95, [," International Journal of Forecasting, Elsevier, vol. 19(4), pages 751-752.
    4. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654, September.
    5. Paulo Rodrigues & Denise Osborn, 1999. "Performance of seasonal unit root tests for monthly data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(8), pages 985-1004.
    6. Hans Franses, Philip & Koehler, Anne B., 1998. "A model selection strategy for time series with increasing seasonal variation," International Journal of Forecasting, Elsevier, vol. 14(3), pages 405-414, September.
    7. jose ramos pires manso, 2004. "Economical Versus Political Cycles In An Iberian Manufacturing Sector," Industrial Organization 0404003, University Library of Munich, Germany.
    8. Beaulieu, J Joseph & Miron, Jeffrey A, 1992. "A Cross Country Comparison of Seasonal Cycles and Business Cycles," Economic Journal, Royal Economic Society, vol. 102(413), pages 772-788, July.
    9. Francisco F. R. Ramos, 1996. "Forecasting market shares using VAR and BVAR models: A comparison of their forecasting performance," Econometrics 9601003, University Library of Munich, Germany.
    10. Clements, Michael & Smith, Jeremy, 1997. "Forecasting Seasonal Uk Consumption Components," Economic Research Papers 268761, University of Warwick - Department of Economics.
    11. Kavussanos, Manolis G. & Alizadeh-M, Amir H., 2002. "Seasonality patterns in tanker spot freight rate markets," Economic Modelling, Elsevier, vol. 19(5), pages 747-782, November.
    12. Pami Dua & Lokendra Kumawat, 2005. "Modelling and Forecasting Seasonality in Indian Macroeconomic Time Series," Working papers 136, Centre for Development Economics, Delhi School of Economics.
    13. Eric Ghysels & Denise R. Osborn & Paulo M. M. Rodrigues, 1999. "Seasonal Nonstationarity and Near-Nonstationarity," CIRANO Working Papers 99s-05, CIRANO.
    14. Ghysels, Éric, 1994. "L’analyse économétrique et la saisonnalité," L'Actualité Economique, Société Canadienne de Science Economique, vol. 70(1), pages 43-62, mars.
    15. Franses,Philip Hans & Dijk,Dick van & Opschoor,Anne, 2014. "Time Series Models for Business and Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521520911, October.
    16. 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.
    17. Albertson, Kevin & Aylen, Jonathan, 2003. "Forecasting the behaviour of manufacturing inventory," International Journal of Forecasting, Elsevier, vol. 19(2), pages 299-311.
    18. John D. Levendis, 2018. "Time Series Econometrics," Springer Texts in Business and Economics, Springer, number 978-3-319-98282-3, April.
    19. Yoshinori Kawasaki, 1996. "A Model Selection Approach to detect Seasonal Unit Roots," Tinbergen Institute Discussion Papers 96-180/7, Tinbergen Institute.
    20. Chan, Chi Kin & Witt, Stephen F. & Lee, Y.C.E. & Song, H., 2010. "Tourism forecast combination using the CUSUM technique," Tourism Management, Elsevier, vol. 31(6), pages 891-897.

    More about this item

    Statistics

    Access and download statistics

    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:intfor:v:7:y:1991:i:2:p:199-208. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijforecast .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.