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On Forecasting Cointegrated Seasonal Time Series

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Author Info

  • Löf, Mårten

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

  • Franses, Philip Hans

    ()
    (Econometric Institute)

Abstract

We analyze periodic and seasonal cointegration models for bivariate quarterly observed time series in an empirical forecasting study. We include both single equation and multiple equation methods. A VAR model in first differences with and without cointegration restrictions is also included in the analysis, where it serves as a benchmark. Our empirical results indicate that the VAR model in first differences without cointegration is best if one-step and four-step ahead forecasts are considered. For longer forecast horizons, however, the periodic and seasonal cointegration models are better. When comparing periodic versus seasonal cointegration models, we find that the seasonal cointegration models tend to yield better forecasts. Finally, there is no clear indication that multiple equation methods improve on single equation methods.

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Bibliographic Info

Paper provided by Stockholm School of Economics in its series Working Paper Series in Economics and Finance with number 350.

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Length: 28 pages
Date of creation: 14 Jan 2000
Date of revision:
Publication status: Forthcoming in International Journal of Forecasting.
Handle: RePEc:hhs:hastef:0350

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Keywords: Periodic Cointegration; Seasonal cointegration; Forecasting;

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References

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  1. repec:fth:erroem:9906/a is not listed on IDEAS
  2. Cubadda, Gianluca, 1999. "Common Cycles in Seasonal Non-stationary Time Series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(3), pages 273-91, May-June.
  3. Boswijk, H. Peter & Franses, Philip Hans & Haldrup, Niels, 1997. "Multiple unit roots in periodic autoregression," Journal of Econometrics, Elsevier, vol. 80(1), pages 167-193, September.
  4. Franses, Philip Hans & Paap, Richard, 1995. "Seasonality and Stochastic Trends in German Consumption and Income, 1960.1-1987.4," Empirical Economics, Springer, vol. 20(1), pages 109-32.
  5. Philip Hans Franses & Michael McAleer, 1998. "Cointegration Analysis of Seasonal Time Series," Journal of Economic Surveys, Wiley Blackwell, vol. 12(5), pages 651-678, December.
  6. 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.
  7. Kleibergen, F.R. & Franses, Ph.H.B.F., 1999. "Cointegration in a periodic vector autoregression," Econometric Institute Research Papers EI 9906-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  8. Johansen, Soren & Schaumburg, Ernst, 1998. "Likelihood analysis of seasonal cointegration," Journal of Econometrics, Elsevier, vol. 88(2), pages 301-339, November.
  9. Boswijk, H Peter & Franses, Philip Hans, 1995. "Periodic Cointegration: Representation and Inference," The Review of Economics and Statistics, MIT Press, vol. 77(3), pages 436-54, August.
  10. Hylleberg, S. & Engle, R.F. & Granger, C.W.J. & Yoo, B.S., 1988. "Seasonal, Integration And Cointegration," Papers 6-88-2, Pennsylvania State - Department of Economics.
  11. Osborn, Denise R., 1991. "The implications of periodically varying coefficients for seasonal time-series processes," Journal of Econometrics, Elsevier, vol. 48(3), pages 373-384, June.
  12. Peter Boswijk, H., 1994. "Testing for an unstable root in conditional and structural error correction models," Journal of Econometrics, Elsevier, vol. 63(1), pages 37-60, July.
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  14. Reimers, Hans-Eggert, 1997. "Forecasting of seasonal cointegrated processes," International Journal of Forecasting, Elsevier, vol. 13(3), pages 369-380, September.
  15. Kunst, Robert M, 1993. "Seasonal Cointegration, Common Seasonals, and Forecasting Seasonal Series," Empirical Economics, Springer, vol. 18(4), pages 761-76.
  16. Wells, J. M., 1997. "Modelling seasonal patterns and long-run trends in U.S. time series," International Journal of Forecasting, Elsevier, vol. 13(3), pages 407-420, September.
  17. Herwartz, Helmut, 1997. "Performance of periodic error correction models in forecasting consumption data," International Journal of Forecasting, Elsevier, vol. 13(3), pages 421-431, September.
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  19. 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.
  20. Lee, Hahn Shik, 1992. "Maximum likelihood inference on cointegration and seasonal cointegration," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 1-47.
  21. 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.
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Citations

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Cited by:
  1. Franses, Ph.H.B.F. & van Dijk, D.J.C., 2001. "The forecasting performance of various models for seasonality and nonlinearity for quarterly industrial production," Econometric Institute Research Papers EI 2001-14, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  2. Darne, Olivier, 2004. "Seasonal cointegration for monthly data," Economics Letters, Elsevier, vol. 82(3), pages 349-356, March.
  3. Cubadda, Gianluca & Omtzigt, Pieter, 2005. "Small-sample improvements in the statistical analysis of seasonally cointegrated systems," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 333-348, April.
  4. Kunst, Robert M., 1997. "Decision Bounds for Data-Admissible Seasonal Models," Economics Series 51, Institute for Advanced Studies.

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