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Forecasting performance of seasonal cointegration models

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  • Lof, Marten
  • Lyhagen, Johan

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.
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  • Lof, Marten & Lyhagen, Johan, 2002. "Forecasting performance of seasonal cointegration models," International Journal of Forecasting, Elsevier, vol. 18(1), pages 31-44.
    • Handle: RePEc:eee:intfor:v:18:y:2002:i:1:p:31-44
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    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 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.
    3. Philip Hans Franses & Robert M. Kunst, 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.
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    6. Lee, Hahn Shik, 1992. "Maximum likelihood inference on cointegration and seasonal cointegration," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 1-47.
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    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).

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    More about this item

    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

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