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On forecasting cointegrated seasonal time series

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  • Lof, Marten
  • Hans Franses, Philip

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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  • Lof, Marten & Hans Franses, Philip, 2001. "On forecasting cointegrated seasonal time series," International Journal of Forecasting, Elsevier, vol. 17(4), pages 607-621.
    • Handle: RePEc:eee:intfor:v:17:y:2001:i:4:p:607-621
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    Cited by:

    1. 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.
    2. P. Geoffrey Allen & Robert Fildes, 2005. "Levels, Differences and ECMs – Principles for Improved Econometric Forecasting," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 881-904, December.
    3. Jacek Kotlowski, 2005. "Money and prices in the Polish economy. Seasonal cointegration approach," Working Papers 20, Department of Applied Econometrics, Warsaw School of Economics.
    4. Franses, Philip Hans & van Dijk, Dick, 2005. "The forecasting performance of various models for seasonality and nonlinearity for quarterly industrial production," International Journal of Forecasting, Elsevier, vol. 21(1), pages 87-102.
    5. Justyna Wr'oblewska, 2020. "Bayesian analysis of seasonally cointegrated VAR model," Papers 2012.14820, arXiv.org, revised Apr 2021.
    6. Anna Czapkiewicz & Marta Stachowicz, 2016. "The long-run relationship between the stock market and main macroeconomic variables in Poland," Managerial Economics, AGH University of Science and Technology, Faculty of Management, vol. 17(1), pages 7-20.
    7. Piotr Białowolski & Tomasz Kuszewski & Bartosz Witkowski, 2014. "Bayesian averaging of classical estimates in forecasting macroeconomic indicators with application of business survey data," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 41(1), pages 53-68, February.
    8. Kunst, Robert M., 1997. "Decision Bounds for Data-Admissible Seasonal Models," Economics Series 51, Institute for Advanced Studies.
    9. Darne, Olivier, 2004. "Seasonal cointegration for monthly data," Economics Letters, Elsevier, vol. 82(3), pages 349-356, March.

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    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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