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

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.

[1]  Todd Litman,et al.  Transportation Elasticities: How Prices and Other Factors Affect Travel Behavior , 2009 .

[2]  R. Fildes,et al.  The Impact of Empirical Accuracy Studies On Time Series Analysis and Forecasting , 1995 .

[3]  C. Dahl,et al.  A survey of oil product demand elasticities for developing countries , 1994 .

[4]  J. Geweke,et al.  THE ESTIMATION AND APPLICATION OF LONG MEMORY TIME SERIES MODELS , 1983 .

[5]  Marno Verbeek,et al.  A Guide to Modern Econometrics , 2000 .

[6]  Steven C. Wheelwright,et al.  Forecasting: Methods and Applications, 3rd Edition , 1998 .

[7]  Guy Judge,et al.  Underlying trends and seasonality in UK energy demand: a sectoral analysis , 2003 .

[8]  D. Hensher.  Climate change, enhanced greenhouse gas emissions and passenger transport: What can we do to make a difference? , 2008 .

[9]  David M. Levine,et al.  Statistics for Managers Using Microsoft® Excel , 1997 .

[10]  Thomas Sterner,et al.  ANALYSING GASOLINE DEMAND ELASTICITIES: A SURVEY , 1991 .

[11]  Richard T. Baillie,et al.  Long memory processes and fractional integration in econometrics , 1996 .

[12]  Robert V. Breunig,et al.  An Exploration of Australian Petrol Demand: Unobservable Habits, Irreversibility and Some Updated Estimates , 2009 .

[13]  N. Wohlgemuth.  World transport energy demand modelling--methodology and elasticities , 1997 .

[14]  David M. Levine,et al.  Statistics for Managers Using Microsoft Excel (5th Edition) , 2007 .

[15]  U. Chakravorty,et al.  Demand for Ground Transportation Fuel and Pricing Policy in Asian Tigers: A Comparative Study of Korea and Taiwan , 1999 .

[16]  Thomas Sterner,et al.  International energy economics , 1992 .

[17]  Lester C. Hunt,et al.  An empirical analysis of energy demand in Namibia , 2006 .

[18]  M. N. Eltony,et al.  Demand for gasoline in Kuwait: An empirical analysis using cointegration techniques , 1995 .

[19]  Douglas C. Frechtling,et al.  Forecasting Tourism Demand: Methods and Strategies , 2001 .

[20]  G. Maddala,et al.  Unit roots, cointegration, and structural change , 1998 .

[21]  David M. Levine,et al.  Statistics for Managers , 2008 .

[22]  M. Espey.  Gasoline demand revisited: an international meta-analysis of elasticities , 1998 .

[23]  J. Stock,et al.  Efficient Tests for an Autoregressive Unit Root , 1992 .

[24]  M. N. Eltony.  Transport Gasoline Demand In Canada , 1993 .

[25]  Molly Espey,et al.  Explaining the Variation in Elasticity Estimates of Gasoline Demand in the United States: A Meta-Analysis , 1996 .

[26]  A. Al-Faris.  Demand for oil products in the GCC countries , 1997 .

[27]  Jakob B. Madsen.  Book review: Basic Econometrics, Damodar N. Gujarati, McGraw-Hill, New York, 1995 , 1998 .

[28]  Thomas Sterner,et al.  GASOLINE TAX POLICY, CARBON EMISSIONS AND THE GLOBAL ENVIRONMENT , 1992 .

[29]  P. Perron,et al.  Lag Length Selection and the Construction of Unit Root Tests with Good Size and Power , 2001 .

[30]  T. Sterner,et al.  Modelling transport fuel demand , 1992 .

[31]  Rodney Samimi.  Road transport energy demand in Australia: A cointegration approach , 1995 .

[32]  Robert Fildes,et al.  Generalising about univariate forecasting methods: Further empirical evidence , 1998 .

[33]  F. Diebold,et al.  Comparing Predictive Accuracy , 1994, Business Cycles.

[34]  Ramakrishnan Ramanathan,et al.  Short- and long-run elasticities of gasoline demand in India: An empirical analysis using cointegration techniques , 1999 .

[35]  P. Goodwin,et al.  Elasticities of Road Traffic and Fuel Consumption with Respect to Price and Income: A Review , 2004 .

[36]  Yetis Sazi Murat,et al.  Use of artificial neural networks for transport energy demand modeling , 2006 .

[37]  F. Birol,et al.  Modelling the transport sector fuel demand for developng economies , 1993 .

[38]  Hilke Kayser,et al.  Gasoline demand and car choice: estimating gasoline demand using household information , 2000 .

[39]  V. Ediger,et al.  ARIMA forecasting of primary energy demand by fuel in Turkey , 2007 .

[40]  Krishan Kumar,et al.  Forecasting Daily Maximum Surface Ozone Concentrations in Brunei Darussalam—An ARIMA Modeling Approach , 2004, Journal of the Air & Waste Management Association.

[41]  B. Bowerman,et al.  Forecasting, time series, and regression : an applied approach , 2005 .

[42]  D. Graham,et al.  The Demand for Automobile Fuel A Survey of Elasticities , 2002 .

[43]  N. Gallini.  Demand for Gasoline in Canada , 1983 .