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Damped Seasonality Factors: Introduction


  • J. S. Armstrong

    (The Wharton School)


Previous research has shown that seasonal factors provide one of the most important ways to improve forecast accuracy. For example, in forecasts over an 18-month horizon for 68 monthly economic series from the M-Competition, Makridakis et al. (1984, Table 14) found that seasonal adjustments reduced the MAPE from 23.0 to 17.7 percent, an error reduction of 23%. On the other hand, research has also shown that seasonal factors sometimes increase forecast errors (e.g., Nelson, 1972). So, when forecasting with a data series measured in intervals that represent part of a year, should one use seasonal factors or not? Statistical tests have been devised to answer this question, and they have been quite useful. However, some people might say that the question is not fair. Why does it have to be either/or? Shouldn^Rt the question be ^Sto what extent should seasonal factors be used for a given series?^T

Suggested Citation

  • J. S. Armstrong, 2005. "Damped Seasonality Factors: Introduction," General Economics and Teaching 0502014, University Library of Munich, Germany.
  • Handle: RePEc:wpa:wuwpgt:0502014
    Note: Type of Document - pdf; pages: 4

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    References listed on IDEAS

    1. Bunn, Derek W. & Vassilopoulos, Angelos I., 1999. "Comparison of seasonal estimation methods in multi-item short-term forecasting," International Journal of Forecasting, Elsevier, vol. 15(4), pages 431-443, October.
    2. Miller, Don M. & Williams, Dan, 2003. "Shrinkage estimators of time series seasonal factors and their effect on forecasting accuracy," International Journal of Forecasting, Elsevier, vol. 19(4), pages 669-684.
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    More about this item


    seasonal factors; forecast; accuracy;

    JEL classification:

    • A - General Economics and Teaching

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