Abstract Forecasting highway consumption of gasoline can be expected to gain greater attention in future years as problems related to energy uncertainties and growing fuel tax revenue shortfalls continue to mount. Although much of the energy forecasting conducted to date has involved fairly large-scale econometric modeling, the immediacy of energy concerns warrants closer attention to near-term time series analyses of gasoline consumption patterns. This paper presents an autoregressive-integrated-moving average (ARIMA) model for forecasting monthly highway energy consumption in the United States using data from the 1977–83 period. Distinct secular and seasonal factors, revolving around 1- and 6-month cycles, emerged from the time series analysis that offer a practical basis for short-term energy forecasting. Efforts to incorporate the interventing effects of the 1979 oil supply disruption on U.S. highway gasoline consumption found that its impacts on the underlying secular, seasonal and stochastic processes behind usage patterns were fairly inconsequential. Comparable short-range models should be integrated into state-level planning programs to closely track possible energy futures at more disaggregate levels.


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    Title :

    Short-run forecasting of highway gasoline consumption in the United States


    Contributors:

    Published in:

    Publication date :

    1985-01-10


    Size :

    9 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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