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Forecasting performance of seasonal cointegration models

  • Löf, Mårten

    ()

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

  • Lyhagen, Johan

    ()

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

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.

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Paper provided by Stockholm School of Economics in its series SSE/EFI Working Paper Series in Economics and Finance with number 336.

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Length: 19 pages
Date of creation: 12 Oct 1999
Date of revision:
Publication status: Forthcoming in International Journal of Forecasting.
Handle: RePEc:hhs:hastef:0336
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  1. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
  2. 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.
  3. Franses, Ph.H.B.F. & Kunst, R.M., 1998. "On the role of seasonal intercepts in seasonal cointegration," Econometric Institute Research Papers EI 9820, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  4. 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 325-30, May.
  5. Johansen, Soren & Schaumburg, Ernst, 1998. "Likelihood analysis of seasonal cointegration," Journal of Econometrics, Elsevier, vol. 88(2), pages 301-339, November.
  6. 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.
  7. Lee, Hahn Shik, 1992. "Maximum likelihood inference on cointegration and seasonal cointegration," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 1-47.
  8. Reimers, Hans-Eggert, 1997. "Forecasting of seasonal cointegrated processes," International Journal of Forecasting, Elsevier, vol. 13(3), pages 369-380, September.
  9. Kunst, Robert M, 1993. "Seasonal Cointegration, Common Seasonals, and Forecasting Seasonal Series," Empirical Economics, Springer, vol. 18(4), pages 761-76.
  10. Kunst, Robert & Neusser, Klaus, 1990. "Cointegration in a Macroeconomic System," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 5(4), pages 351-65, Oct.-Dec..
  11. Wells, John M., 1997. "Business Cycles, Seasonal Cycles, and Common Trends," Journal of Macroeconomics, Elsevier, vol. 19(3), pages 443-469, July.
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