ARMA models and the Box-Jenkins methodology

Spyros Makridakis, Michèle Hibon

    Research output: Contribution to journalArticlepeer-review

    103 Citations (Scopus)


    The purpose of this paper is to apply the Box-Jenkins methodology to ARIMA models and determine the reasons why in empirical tests it is found that the post-sample forecasting the accuracy of such models is generally worse than much simpler time series methods. The paper concludes that the major problem is the way of making the series stationary in its mean (i.e. the method of differencing) that has been proposed by Box and Jenkins. If alternative approaches are utilized to remove and extrapolate the trend in the data, ARMA models outperform the models selected through Box-Jenkins methodology. In addition, it is shown that using ARMA models to seasonally adjusted data slightly improves post-sample accuracies while simplifying the use of ARMA models. It is also confirmed that transformations slightly improve post-sample forecasting accuracy, particularly for long forecasting horizons. Finally, it is demonstrated that AR(1), AR(2) and ARMA(1,1) models can produce more accurate post-sample forecasts than those found through the application of Box-Jenkins methodology.

    Original languageEnglish
    Pages (from-to)147-163
    Number of pages17
    JournalJournal of Forecasting
    Issue number3
    Publication statusPublished - 1 Jan 1997


    • ARMA models
    • Box-Jenkins
    • Empirical studies
    • M-Competition
    • Time-series forecasting


    Dive into the research topics of 'ARMA models and the Box-Jenkins methodology'. Together they form a unique fingerprint.

    Cite this