Forecasting automobile petrol demand in Australia: An evaluation of empirical models
John Rose () and
David Hensher ()
Transportation Research Part A: Policy and Practice, 2010, vol. 44, issue 1, 16-38
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.
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 (search for similar items in EconPapers)
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