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The Regional Demand for Petrol in Australia

Author

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  • W. A. DONNELLY
Abstract
The regional demand for petrol in Australia is estimated using quarterly data on a State basis for the period from the September quarter 1958 through the June quarter 1981. A dynamic demand model is postulated and estimates obtained using an iterative Zellner procedure. The results suggest that per capita petrol demand is not income elastic as reported in other studies, and support the hypothesis that demand is price inelastic. In addition, the States exhibit statistically significant differential responses to price and income shocks. This implies that the specification of a single national demand function may be misleading.

Suggested Citation

  • W. A. Donnelly, 1982. "The Regional Demand for Petrol in Australia," The Economic Record, The Economic Society of Australia, vol. 58(4), pages 317-327, December.
  • Handle: RePEc:bla:ecorec:v:58:y:1982:i:4:p:317-327
    DOI: 10.1111/j.1475-4932.1982.tb00382.x
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    References listed on IDEAS

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    1. Bart D. Ostro & Joel L. Naroff, 1980. "Decentralization and the Demand for Gasoline," Land Economics, University of Wisconsin Press, vol. 56(2), pages 169-180.
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    Cited by:

    1. Robert V. Breunig & Carol Gisz, 2009. "An Exploration of Australian Petrol Demand: Unobservable Habits, Irreversibility and Some Updated Estimates," The Economic Record, The Economic Society of Australia, vol. 85(268), pages 73-91, March.
    2. Brons, Martijn & Nijkamp, Peter & Pels, Eric & Rietveld, Piet, 2008. "A meta-analysis of the price elasticity of gasoline demand. A SUR approach," Energy Economics, Elsevier, vol. 30(5), pages 2105-2122, September.
    3. Martijn Brons & Peter Nijkamp & Eric Pels & Piet Rietveld, 2006. "A Meta-analysis of the Price Elasticity of Gasoline Demand. A System of Equations Approach," Tinbergen Institute Discussion Papers 06-106/3, Tinbergen Institute.
    4. Shaw, Charles, 2020. "Econometric Analysis of Demand for Petrol in India, 1966-2019," MPRA Paper 104797, University Library of Munich, Germany.
    5. Giuliano Galimberti & Gabriele Soffritti, 2020. "Seemingly unrelated clusterwise linear regression," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(2), pages 235-260, June.

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