Developing a Time Series Forecasting Model for Monthly Gasoline Consumption in Iran: An Application of the Seasonal Unit Root Test

Articles in Press

Document Type : Original Research

Authors
ebrahim ensan
AmirHosein Chizari
atefeh karimi
Department of Agricultural Economics, Colleges of Agriculture & Natural Resources, University of Tehran, Islamic Republic of Iran
Abstract
Energy consumption in Iran has risen sharply in recent decades, with the transportation sector as one of the largest consumers due to weak public transit and high reliance on gasoline. Limited refining capacity, sanctions, and rapid demand growth have widened the gap between production and consumption, making accurate forecasts critical for planning, budgeting, and policy design. This study forecasts monthly gasoline consumption in Iran using univariate seasonal time series models and data from April 1998 to March 2024. Seasonal unit root tests by Beaulieu–Miron and Taylor indicated a nonseasonal unit root but no seasonal unit roots, implying the need for first order differencing. Two models were then estimated: an autoregressive regression with monthly dummies and a seasonal autoregressive integrated moving average (SARIMA). out-of-sample forecasts (April 2023–March 2024) show that SARIMA outperformed the regression model with a Mean Absolute Percentage Error (MAPE) of 2.15% compared to 18.34%. Forecasts for April 2024–March 2027 indicate cumulative growth of 6.5% in gasoline consumption, averaging 2.15% annually over 115% higher than global energy growth projected by the International Energy Agency. These findings highlight the urgency of strengthening refining capacity, improving fuel efficiency, expanding public transport, and implementing rational pricing policies.
Keywords
Forecasting
Gasoline consumption
Iran
SARIMA
Seasonal unit root test
Subjects
 
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Journal of Agricultural Science and Technology

Articles in Press, Accepted Manuscript
Available Online from 07 October 2026