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Estimating dynamics of US demand for major fossil fuels

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  • Miljkovic, Dragan
  • Dalbec, Nathan
  • Zhang, Lei

Abstract

Long-run demand relationships among fossil fuels in the United States were investigated using annual data covering 1918 through 2013. Due to the endogeneity problem among the variables of interest, as indicated by the findings from the Granger Causality test, weak exogeneity test, and Directed Acyclic Graphs, the use of the seemingly unrelated regression (SUR) method was deemed appropriate. The SUR model demonstrated that there was low level of substitutability among fossil fuels, but the small magnitude of the estimated coefficient indicates that natural gas, oil, and coal are more properly classified as independent goods than as substitutes of each other within the US market. Income elasticities for all three fossil fuels indicate that they are normal goods. Several external shocks have significant impact on demand for each of the fossil fuels. Slightly lower explanatory power of oil demand equation may be explained with the fact that the model did not include US oil imports although the US economy has been dependent, to some degree, on imported oil.

Suggested Citation

  • Miljkovic, Dragan & Dalbec, Nathan & Zhang, Lei, 2016. "Estimating dynamics of US demand for major fossil fuels," Energy Economics, Elsevier, vol. 55(C), pages 284-291.
    • Handle: RePEc:eee:eneeco:v:55:y:2016:i:c:p:284-291
    DOI: 10.1016/j.eneco.2016.02.018
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    2. Tao, Yong & Lin, Li & Wang, Hanjie & Hou, Chen, 2023. "Superlinear growth and the fossil fuel energy sustainability dilemma: Evidence from six continents," Structural Change and Economic Dynamics, Elsevier, vol. 66(C), pages 39-51.
    3. Barrera, Emiliano Lopez & Miljkovic, Dragan, 2022. "The link between the two epidemics provides an opportunity to remedy obesity while dealing with Covid-19," Journal of Policy Modeling, Elsevier, vol. 44(2), pages 280-297.
    4. Esmaeili, Parisa & Rafei, Meysam, 2021. "Dynamics analysis of factors affecting electricity consumption fluctuations based on economic conditions: Application of SVAR and TVP-VAR models," Energy, Elsevier, vol. 226(C).
    5. Copiello, Sergio & Grillenzoni, Carlo, 2017. "Is the cold the only reason why we heat our homes? Empirical evidence from spatial series data," Applied Energy, Elsevier, vol. 193(C), pages 491-506.
    6. Dragan Miljkovic & Jungho Baek, 2019. "Monetary impacts and overshooting of energy prices: the case of the U.S. coal prices," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 32(3), pages 317-322, November.
    7. Baek, Jungho & Miljkovic, Dragan, 2018. "Monetary policy and overshooting of oil prices in an open economy," The Quarterly Review of Economics and Finance, Elsevier, vol. 70(C), pages 1-5.
    8. Dragan Miljkovic & Cole Goetz, 2020. "Destabilizing role of futures markets on North American hard red spring wheat spot prices," Agricultural Economics, International Association of Agricultural Economists, vol. 51(6), pages 887-897, November.
    9. Goetz, Cole & Miljkovic, Dragan & Barabanov, Nikita, 2021. "New empirical evidence in support of the theory of price volatility of storable commodities under rational expectations in spot and futures markets," Energy Economics, Elsevier, vol. 100(C).
    10. Dragan Miljkovic & Cole Goetz, 2023. "Futures markets and price stabilisation: An analysis of soybeans markets in North America," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 67(1), pages 104-117, January.
    11. Miljkovic, Dragan & Goetz, Cole, 2020. "The effects of futures markets on oil spot price volatility in regional US markets," Applied Energy, Elsevier, vol. 273(C).
    12. Barnor, Kodjo & Caton, James & Miljkovic, Dragan, 2023. "The role of funding on research and science: The impact of glyphosate herbicides on health and the environment," Journal of Policy Modeling, Elsevier, vol. 45(1), pages 103-120.

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    More about this item

    Keywords

    Long-run demand; Fossil fuels; United States; Time-series econometrics;
    All these keywords.

    JEL classification:

    • Q31 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - Demand and Supply; Prices
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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