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Seasonality, Nonstationarity And The Forecasting Of Monthly Time Series

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  • Franses, P. H.

Abstract

In this paper the focus is on two forecasting models for a monthly time series. The first model requires that the variable is first order and seasonally differenced. The second model considers the series only in its first order differences, while seasonality is modeled with a constant and seasonal dummies. A method to empirically distinguish between these two models is presented. The relevance of this method is established by simulation results, as well as empirical evidence, which show that,. firstly, conventional autocorrelation checks are often not discriminative, and, secondly, that considering the first model while the second is more appropriate yields a deterioration of forecasting performance.

Suggested Citation

  • Franses, P. H., 1990. "Seasonality, Nonstationarity And The Forecasting Of Monthly Time Series," Econometric Institute Archives 272481, Erasmus University Rotterdam.
    • Handle: RePEc:ags:eureia:272481
    DOI: 10.22004/ag.econ.272481
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    1. 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.
    2. Osborn, Denise R., 1990. "A survey of seasonality in UK macroeconomic variables," International Journal of Forecasting, Elsevier, vol. 6(3), pages 327-336, October.
    3. Bodo, Giorgio & Signorini, Luigi Federico, 1987. "Short-term forecasting of the industrial production index," International Journal of Forecasting, Elsevier, vol. 3(2), pages 245-259.
    4. Granger, C. W. J. & Newbold, Paul, 1986. "Forecasting Economic Time Series," Elsevier Monographs, Elsevier, edition 2, number 9780122951831 edited by Shell, Karl.
    5. Heuts, R. M. J. & Bronckers, J. H. J. M., 1988. "Forecasting the Dutch heavy truck market : A multivariate approach," International Journal of Forecasting, Elsevier, vol. 4(1), pages 57-79.
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