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Forecasting automobile petrol demand in Australia: An evaluation of empirical models

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  • Li, Zheng
  • Rose, John M.
  • Hensher, David A.
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    Abstract

    Transport fuel consumption and its determinants have received a great deal of attention since the early 1970s. In the literature, different types of modelling methods have been used to estimate petrol demand, each having methodological strengths and weaknesses. This paper is motivated by an ongoing need to review the effectiveness of empirical fuel demand forecasting models, with a focus on theoretical as well as practical considerations in the model-building processes of different model forms. We consider a linear trend model, a quadratic trend model, an exponential trend model, a single exponential smoothing model, Holt's linear model, Holt-Winters' model, a partial adjustment model (PAM), and an autoregressive integrated moving average (ARIMA) model. More importantly, the study identifies the difference between forecasts and actual observations of petrol demand in order to identify forecasting accuracy. Given the identified best-forecasting model, Australia's automobile petrol demand from 2007 through to 2020 is presented under the "business-as-usual" scenario.

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    Bibliographic Info

    Article provided by Elsevier in its journal Transportation Research Part A: Policy and Practice.

    Volume (Year): 44 (2010)
    Issue (Month): 1 (January)
    Pages: 16-38

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    Handle: RePEc:eee:transa:v:44:y:2010:i:1:p:16-38

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    Keywords: Petrol demand forecasting Automobiles Time series data The partial adjustment model The autoregressive integrated moving average model Elasticities Trend-fitting approaches Exponential smoothing Forecasting effectiveness;

    References

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    Cited by:
    1. Gkritza, Konstantina & Karlaftis, Matthew G. & Mannering, Fred L., 2011. "Estimating multimodal transit ridership with a varying fare structure," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(2), pages 148-160, February.
    2. Jian Chai & Shubin Wang & Shouyang Wang & Ju’e Guo, 2012. "Demand Forecast of Petroleum Product Consumption in the Chinese Transportation Industry," Energies, MDPI, Open Access Journal, vol. 5(3), pages 577-598, March.
    3. Al-Ghandoor, Ahmed & Jaber, Jamal & Al-Hinti, Ismael & Abdallat, Yousef, 2013. "Statistical assessment and analyses of the determinants of transportation sector gasoline demand in Jordan," Transportation Research Part A: Policy and Practice, Elsevier, vol. 50(C), pages 129-138.
    4. Willem H. Boshoff, 2011. "Gasoline, diesel fuel and jet fuel demand in South Africa," Working Papers 226, Economic Research Southern Africa.
    5. Pelinescu, Elena & Anton, Lucian Vasile & Ionescu, Raluca & Tasca, Radu, 2010. "The Analysis of Local Budgets and Their Importance in the Fight Against the Economic Crisis Effects," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(5), pages 17-32.
    6. Bakhat, Mohcine & Rosselló, Jaume, 2013. "Evaluating a seasonal fuel tax in a mass tourism destination: A case study for the Balearic Islands," Energy Economics, Elsevier, vol. 38(C), pages 12-18.
    7. Nazneen Ferdous & Abdul Pinjari & Chandra Bhat & Ram Pendyala, 2010. "A comprehensive analysis of household transportation expenditures relative to other goods and services: an application to United States consumer expenditure data," Transportation, Springer, vol. 37(3), pages 363-390, May.
    8. Keshavarzian, Maryam & Kamali Anaraki, Sara & Zamani, Mehrzad & Erfanifard, Ali, 2012. "Projections of oil demand in road transportation sector on the basis of vehicle ownership projections, worldwide: 1972–2020," Economic Modelling, Elsevier, vol. 29(5), pages 1979-1985.

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