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Shocks to supply and demand in the oil market, the equilibrium oil price, and country responses in economic indicators

  • Tamara V. Teplova
  • Vladimir V. Lysenko
  • Tatiana V. Sokolova
Original Paper
  • 35 Downloads

Abstract

We develop a model to forecast the equilibrium price in the oil market by balancing demand and supply at the level of interaction of the largest oil-consuming and -producing countries. Our model is based on the global vector autoregression methodology and allows us to make a medium-term forecast of the equilibrium oil price in dynamics analyzing the co-movement of oil demand and supply in various countries, in view of possible shocks from countries and companies. The proposed model allows us to reveal responses in economic indicators in various countries to changes in the equilibrium oil price. Our model covers 47 countries, including the member countries of the Organization of Petroleum Exporting Countries (OPEC), the Commonwealth of Independent States (CIS), and the largest oil-consuming countries. The majority of models analyze the only largest market players, but we consider the member countries of the CIS (Russia, Kazakhstan, and Azerbaijan) and OPEC member countries such as Iraq, the United Arab Emirates (UAE), Qatar, Venezuela, Algeria, Nigeria, and Angola. The test results on economic consequences of a shock to oil supplies from the largest producer (Saudi Arabia) and a shock to oil demand from the largest consumer (China) are of empirical interest.

Keywords

Oil demand and supply Equilibrium oil price Shocks of oil supply and demand Oil price forecasting models Global vector autoregression methodology 

References

  1. 1.
    Alquist, R., Kilian, L.: What do we learn from the price of crude oil futures? J Appl Econ 25(4), 539–573 (2010)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Baumeister, C., Hamilton, J.: Sign restrictions, structural vector autoregressions, and useful prior information. Econometrica 83(5), 1963–1999 (2015)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Baumeister, C., Hamilton, J.: Structural interpretation of vector autoregressions with incomplete identification: revisiting the role of oil supply and demand shocks. NBER working paper, 24167 (2017). URL: http://nber.org/papers/w24167
  4. 4.
    Baumeister, C., Peersman, G., Van Robays, I.: The economic consequences of oil shocks: Differences across countries and time. In: Fry, R., Jones, C., Kent, C. (eds.) Inflation in an Era of Relative Price Shocks. Reserve Bank of Australia, Sydney (2010)Google Scholar
  5. 5.
    BP: statistical review of world energy. BP, London, UK (2016)Google Scholar
  6. 6.
    Chudik, A., Pesaran, M.H.: Theory and practice of GVAR modelling. J Econ Surv 30(1), 165–197 (2016)CrossRefGoogle Scholar
  7. 7.
    Coppola, A.: Forecasting oil price movements: exploiting the information in the futures market. J Futures Mark 28(1), 34–56 (2008)CrossRefGoogle Scholar
  8. 8.
    Dees, S., Mauro, F.D., Pesaran, M.H., Smith, L.V.: Exploring the international linkages of the euro area: a global VAR analysis. J Appl Econ 22, 1–38 (2007)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Dees, S., Pesaran. M.H., Smith, L.V., Smith, R.: Supply, demand and monetary policy shocks in a multi-country new Keynesian model. ECB working paper, 1239. European Cental Bank, Frankfurt am Main, Germany (2010)Google Scholar
  10. 10.
    Dybczak, K., Voňka, D., van der Windt, N.: The effect of oil price shocks on the Czech Economy. Czech Natl Bank Work Pap Ser 5. Praha, Czech Republic (2008)Google Scholar
  11. 11.
    Fattouh, B., Kilian, L., Mahadeva, L.: The role of speculation in oil markets: what have we learned so far? Energy J 34(3), 7–33 (2013)CrossRefGoogle Scholar
  12. 12.
    Ghadimi, H.: A dynamic CGE analysis of exhaustible resources: the case of an oil exporting developing country. Research paper 2006-7 (2006)Google Scholar
  13. 13.
    Gregory, A.W., Head, A.C., Raynauld, J.: Measuring world business cycles. Int Econ Rev 38(3), 677–702 (1997)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Hagen, R.: How is the international price of a particular crude determined? OPEC Rev 18(1), 127–135 (1994)CrossRefGoogle Scholar
  15. 15.
    Haugom, E., Mydland, O., Pichler, A.: Long term oil prices. Energy Econ 58, 84–94 (2016)CrossRefGoogle Scholar
  16. 16.
    Hea, Y., Hongb, Y.: Unbiasedness and market Query efficiency of crude oil futures markets: a revisit. Unpublished manuscript (2011)Google Scholar
  17. 17.
    Hubbert, M.K.: Nuclear energy and the fossil fuels. Drill Prod Prac 95, 1–57 (1956)Google Scholar
  18. 18.
    International energy agency: world energy model documentation. http://worldenergyoutlook.org/media/weowebsite/2015/WEM_Documentation_WEO2015.pdf (2015). Accessed 1 Feb 2018
  19. 19.
    International monetary fund: direction of trade statistics. http://data.imf.org/?sk=9D6028D4-F14A-464C-A2F2-59B2CD424B85. Accessed 1 Feb 2018
  20. 20.
    Kilian, L.: A comparison of the effects of exogenous oil supply shocks on output and inflation in the G7 countries. J Eur Econ Assoc 6(1), 78–121 (2008)CrossRefGoogle Scholar
  21. 21.
    Kilian, L.: Exogenous oil supply shocks: how big are they and how much do they matter for the US economy? Rev Econ Stat 90(2), 216–240 (2008)CrossRefGoogle Scholar
  22. 22.
    Koop, G., Korobilis, D.: Forecasting inflation using dynamic model averaging. Int Econ Rev 53(3), 867–886 (2012)MathSciNetCrossRefGoogle Scholar
  23. 23.
    McDonald, S.: A computable general equilibrium (CGE) analysis of the impact of an oil price increase in South Africa. Working paper, 15633 (2005)Google Scholar
  24. 24.
    Mohaddes, K., Pesaran, M.H.: Country-specific oil supply shocks and the global economy: a counterfactual analysis. Energy Econ 59, 382–399 (2016)CrossRefGoogle Scholar
  25. 25.
    Mohaddes, K., Pesaran, M.H.: Oil prices and the global economy: is it different this time around? IMF working paper no. 16/210 (2016)Google Scholar
  26. 26.
    Naser, H.: Estimating and forecasting the real prices of crude oil: a data rich model using a dynamic model averaging (DMA) approach. Energy Econ 56, 75–87 (2016)CrossRefGoogle Scholar
  27. 27.
    Peersman, G., Robays, I.V.: Oil and the Euro area economy. Econ Policy 24(60), 603–651 (2012)CrossRefGoogle Scholar
  28. 28.
    Peersman, G., Van Robays, I.: Oil and the Euro area economy. Econ Policy 24, 603–651 (2009)CrossRefGoogle Scholar
  29. 29.
    Pesaran, M.H., Schuermann, T., Weiner, S.M.: Modeling regional interdependencies using a global error-correcting macroeconomic model. J Bus Econ Stat 22, 129–162 (2004)CrossRefGoogle Scholar
  30. 30.
    Pesaran, M.H., Schuermann, T., Treutler, B., Weiner, S.M.: Macroeconomic dynamics and credit risk: a global perspective. J Money Credit Bank 38(5), 1211–1261 (2006)CrossRefGoogle Scholar
  31. 31.
    Pesaran, M.H., Smith, V., Smith, R.P.: What if the UK or Sweden had joined the euro in 1999? An empirical evaluation using a global VAR. Int J Financ Econ 12, 55–87 (2007)CrossRefGoogle Scholar
  32. 32.
    Pesaran, M.H., Schuermann, T., Smith, V.: Rejoinder to comments on forecasting economic and financial variables with global VARs. Int J Forecast 25(4), 703–715 (2009)CrossRefGoogle Scholar
  33. 33.
    Smith, L.V., Galesi, A.: GVAR toolbox 2.0, https://sites.google.com/site/gvarmodelling/gvar-toolbox (2014). Accessed 1 Feb 2018
  34. 34.
    Stevens, P.: The determination of oil prices 1945–1995: a diagrammatic interpretation. Energy Policy 23(10), 861–870 (1995)CrossRefGoogle Scholar
  35. 35.
    International energy outlook. US energy information administration. Washington, US (2016)Google Scholar
  36. 36.
    Zagaglia, P.: Macroeconomic factors and oil futures prices: a data-rich model. Energy Econ 32(2), 409–417 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Tamara V. Teplova
    • 1
  • Vladimir V. Lysenko
    • 2
  • Tatiana V. Sokolova
    • 1
  1. 1.Faculty of Economic SciencesNational Research University Higher School of EconomicsMoscowRussia
  2. 2.Federal State Unitary Enterprise ‘Central Scientific Research Institute of Shipbuilding Industry ‘Center’MoscowRussia

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