IDEAS home Printed from https://ideas.repec.org/p/hhs/hastef/0336.html
   My bibliography  Save this paper

Forecasting performance of seasonal cointegration models

Author

Listed:
  • Löf, Mårten

    () (Dept. of Economic Statistics, Stockholm School of Economics)

  • Lyhagen, Johan

    () (Dept. of Economic Statistics, Stockholm School of Economics)

Abstract

Forecasts from seasonal cointegration models are compared with those from a standard cointegration model based on first differences and seasonal dummies. The effects of restricting or not restricting seasonal intercepts in the seasonal cointegration models are examined as well as the recently proposed specification and estimation procedure for the annual frequency by Johansen and Schaumburg (1999). The data generating process used in the Monte Carlo simulation is based on an empirical six-dimensional macroeconomic data set. Results show that the seasonal cointegration model improves forecasting accuracy, compared with the standard cointegration model, even in small samples and if short forecast horizons are considered. Furthermore, the specification suggested by Johansen and Schaumburg seems to work better than the original model presented by Lee (1992). An empirical forecasting example confirm most of the results found in the Monte Carlo study.

Suggested Citation

  • Löf, Mårten & Lyhagen, Johan, 1999. "Forecasting performance of seasonal cointegration models," SSE/EFI Working Paper Series in Economics and Finance 336, Stockholm School of Economics.
  • Handle: RePEc:hhs:hastef:0336
    as

    Download full text from publisher

    File URL: http://swopec.hhs.se/hastef/papers/hastef0336.pdf.zip
    Download Restriction: no

    File URL: http://swopec.hhs.se/hastef/papers/hastef0336.pdf
    Download Restriction: no

    File URL: http://swopec.hhs.se/hastef/papers/hastef0336.ps.zip
    Download Restriction: no

    File URL: http://swopec.hhs.se/hastef/papers/hastef0336.ps
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Johansen, Soren & Schaumburg, Ernst, 1998. "Likelihood analysis of seasonal cointegration," Journal of Econometrics, Elsevier, vol. 88(2), pages 301-339, November.
    2. Kunst, Robert M, 1993. "Seasonal Cointegration, Common Seasonals, and Forecasting Seasonal Series," Empirical Economics, Springer, vol. 18(4), pages 761-776.
    3. Lee, Hahn S. & Siklos, Pierre L., 1995. "A note on the critical values for the maximum likelihood (seasonal) cointegration tests," Economics Letters, Elsevier, vol. 49(2), pages 137-145, August.
    4. 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.
    5. Reimers, Hans-Eggert, 1997. "Forecasting of seasonal cointegrated processes," International Journal of Forecasting, Elsevier, vol. 13(3), pages 369-380, September.
    6. Kunst, Robert M, 1993. "Seasonal Cointegration in Macroeconomic Systems: Case Studies for Small and Large European Countries," The Review of Economics and Statistics, MIT Press, vol. 75(2), pages 325-330, May.
    7. Wells, John M., 1997. "Business Cycles, Seasonal Cycles, and Common Trends," Journal of Macroeconomics, Elsevier, vol. 19(3), pages 443-469, July.
    8. Kunst, Robert & Neusser, Klaus, 1990. "Cointegration in a Macroeconomic System," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 5(4), pages 351-365, Oct.-Dec..
    9. Franses, Philip Hans & Kunst, Robert M, 1999. " On the Role of Seasonal Intercepts in Seasonal Cointegration," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(3), pages 409-433, August.
    10. Lee, Hahn Shik, 1992. "Maximum likelihood inference on cointegration and seasonal cointegration," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 1-47.
    11. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
    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. Mårten Löf & Johan Lyhagen, 2003. "On seasonal error correction when the processes include different numbers of unit roots," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(5), pages 377-389.
    2. Darne, Olivier, 2004. "Seasonal cointegration for monthly data," Economics Letters, Elsevier, vol. 82(3), pages 349-356, March.
    3. Nyblom, Jukka & Suomala, Jaakko, 2014. "Tests for real and complex unit roots in vector autoregressive models," Journal of Multivariate Analysis, Elsevier, vol. 130(C), pages 224-239.
    4. Ozlem Tasseven, 2009. "Seasonal Co-integration An Extension of the Johansen and Schaumburg Approach with an Exclusion Test," Panoeconomicus, Savez ekonomista Vojvodine, Novi Sad, Serbia, vol. 56(1), pages 39-53, March.
    5. Xu, Yun & Sheldon, Ian M., 2005. "Pricing to Market, (Seasonal) Cointegration and US Agricultural Exports," 2005 Annual meeting, July 24-27, Providence, RI 19377, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).

    More about this item

    Keywords

    Seasonal cointegration; Monte Carlo; Forecasting;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:hhs:hastef:0336. 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: (Helena Lundin). General contact details of provider: http://edirc.repec.org/data/erhhsse.html .

    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.