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Mass and energy-capital conservation equations to forecast the oil price evolution with accumulation or depletion of the resources

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  • Gori, Fabio

Abstract

The present work extends the approach of using the mass and energy-capital conservation equations to forecast the price evolution of oil when accumulation or depletion is present. The price evolution is then dependent on the consumption rate of the oil, besides the ratio of mass extraction to mass consumption rates, and the usual economic parameters, e.g. the interest rates of non-extracted and extracted resources. The main conclusions are that a ratio of mass extraction to consumption rates different from unity, i.e. when accumulation or depletion of the oil is present, can modify the approach of the oil price forecast without accumulation or depletion of the resources.

Suggested Citation

  • Gori, Fabio, 2016. "Mass and energy-capital conservation equations to forecast the oil price evolution with accumulation or depletion of the resources," Energy, Elsevier, vol. 116(P1), pages 746-760.
    • Handle: RePEc:eee:energy:v:116:y:2016:i:p1:p:746-760
    DOI: 10.1016/j.energy.2016.10.018
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    References listed on IDEAS

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    1. Mingming, Tang & Jinliang, Zhang, 2012. "A multiple adaptive wavelet recurrent neural network model to analyze crude oil prices," Journal of Economics and Business, Elsevier, vol. 64(4), pages 275-286.
    2. Hallock, John L. & Wu, Wei & Hall, Charles A.S. & Jefferson, Michael, 2014. "Forecasting the limits to the availability and diversity of global conventional oil supply: Validation," Energy, Elsevier, vol. 64(C), pages 130-153.
    3. repec:aen:journl:2006v27-04-a04 is not listed on IDEAS
    4. Harold Hotelling, 1931. "The Economics of Exhaustible Resources," Journal of Political Economy, University of Chicago Press, vol. 39(2), pages 137-137.
    5. Alvarez-Ramirez, Jose & Cisneros, Myriam & Ibarra-Valdez, Carlos & Soriano, Angel, 2002. "Multifractal Hurst analysis of crude oil prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 313(3), pages 651-670.
    6. repec:aen:journl:2009v30-02-a09 is not listed on IDEAS
    7. Fiévet, L. & Forró, Z. & Cauwels, P. & Sornette, D., 2015. "A general improved methodology to forecasting future oil production: Application to the UK and Norway," Energy, Elsevier, vol. 79(C), pages 288-297.
    8. Abramson, Bruce & Finizza, Anthony, 1991. "Using belief networks to forecast oil prices," International Journal of Forecasting, Elsevier, vol. 7(3), pages 299-315, November.
    9. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    10. Movagharnejad, Kamyar & Mehdizadeh, Bahman & Banihashemi, Morteza & Kordkheili, Masoud Sheikhi, 2011. "Forecasting the differences between various commercial oil prices in the Persian Gulf region by neural network," Energy, Elsevier, vol. 36(7), pages 3979-3984.
    11. Masoud Rabbani & S.M. Ghoreyshi & H. Rafiei & M. Ghazanfari, 2012. "Energy consumption forecasting using a bi-objective fuzzy linear regression model," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 13(1), pages 1-18.
    12. He, Kaijian & Yu, Lean & Lai, Kin Keung, 2012. "Crude oil price analysis and forecasting using wavelet decomposed ensemble model," Energy, Elsevier, vol. 46(1), pages 564-574.
    13. Manel Hamdi & Chaker Aloui, 2015. "Forecasting Crude Oil Price Using Artificial Neural Networks: A Literature Survey," Economics Bulletin, AccessEcon, vol. 35(2), pages 1339-1359.
    14. repec:aen:journl:1994v15-02-a01 is not listed on IDEAS
    15. Gallo, Andres & Mason, Paul & Shapiro, Steve & Fabritius, Michael, 2010. "What is behind the increase in oil prices? Analyzing oil consumption and supply relationship with oil price," Energy, Elsevier, vol. 35(10), pages 4126-4141.
    16. Gori, F. & Ludovisi, D. & Cerritelli, P.F., 2007. "Forecast of oil price and consumption in the short term under three scenarios: Parabolic, linear and chaotic behaviour," Energy, Elsevier, vol. 32(7), pages 1291-1296.
    17. Ghaffari, Ali & Zare, Samaneh, 2009. "A novel algorithm for prediction of crude oil price variation based on soft computing," Energy Economics, Elsevier, vol. 31(4), pages 531-536, July.
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