IDEAS home Printed from https://ideas.repec.org/p/mib/wpaper/223.html

The oil price-macroeconomy relationship since the mid-1980s: A global perspective

Author

Listed:
  • Claudio Morana

Abstract

In this paper the oil price-macroeconomy relationship is investigated from a global perspective, by means of a large scale macro-financial-econometric model. In addition to real activity, fiscal and monetary policy responses and labor and financial markets are considered as well. We find that oil market shocks would have contributed to slowing down economic growth since the first Persian Gulf War episode. Among oil market shocks, supply side disturbances were the largest contributor to macro-financial fluctuations, accounting for up to 12% of real activity variance. The latter shocks would have exercised recessionary effects during the first and second Persian Gulf War and 2008 oil price episodes; preferences, speculative and volatility shocks would have also contributed to exacerbate the recessionary episodes. As long as oil supply will keep expanding at a lower pace than required by demand conditions, a recessionary bias, determined by higher and more uncertain real oil prices, may then be expected to persist also in the near future.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Claudio Morana, 2013. "The oil price-macroeconomy relationship since the mid-1980s: A global perspective," Working Papers 223, University of Milano-Bicocca, Department of Economics, revised Feb 2013.
    • Handle: RePEc:mib:wpaper:223
    as

    Download full text from publisher

    File URL: http://repec.dems.unimib.it/repec/pdf/mibwpaper223.pdf
    File Function: First version, 2013
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen, Shiyi & Chen, Dengke & Härdle, Wolfgang Karl, 2014. "The influence of oil price shocks on China's macro-economy: A perspective of international trade," SFB 649 Discussion Papers 2014-063, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    2. Hegerty, Scott W., 2016. "Commodity-price volatility and macroeconomic spillovers: Evidence from nine emerging markets," The North American Journal of Economics and Finance, Elsevier, vol. 35(C), pages 23-37.
    3. Lorusso, Marco & Pieroni, Luca, 2018. "Causes and consequences of oil price shocks on the UK economy," Economic Modelling, Elsevier, vol. 72(C), pages 223-236.
    4. Abdollahi, Hooman & Ebrahimi, Seyed Babak, 2020. "A new hybrid model for forecasting Brent crude oil price," Energy, Elsevier, vol. 200(C).
    5. Morana, Claudio, 2017. "Macroeconomic and financial effects of oil price shocks: Evidence for the euro area," Economic Modelling, Elsevier, vol. 64(C), pages 82-96.
    6. Timilsina, Govinda R., 2015. "Oil prices and the global economy: A general equilibrium analysis," Energy Economics, Elsevier, vol. 49(C), pages 669-675.
    7. Akdoğan, Kurmaş, 2020. "Fundamentals versus speculation in oil market: The role of asymmetries in price adjustment?," Resources Policy, Elsevier, vol. 67(C).
    8. Bagliano, Fabio C. & Morana, Claudio, 2014. "Determinants of US financial fragility conditions," Research in International Business and Finance, Elsevier, vol. 30(C), pages 377-392.
    9. Shrestha, Anil & Mustafa, Andy Ali & Htike, Myo Myo & You, Vithyea & Kakinaka, Makoto, 2022. "Evolution of energy mix in emerging countries: Modern renewable energy, traditional renewable energy, and non-renewable energy," Renewable Energy, Elsevier, vol. 199(C), pages 419-432.
    10. Chul-Yong Lee & Sung-Yoon Huh, 2017. "Forecasting Long-Term Crude Oil Prices Using a Bayesian Model with Informative Priors," Sustainability, MDPI, vol. 9(2), pages 1-15, January.
    11. Ramaprasad Bhar & Anastasios G. Malliaris & Mary Malliaris, 2021. "What Has Driven the U.S. Monthly Oil Production Since 2009? Empirical Results from Two Modeling Approaches," JRFM, MDPI, vol. 14(2), pages 1-11, February.
    12. Cheng, Fangzheng & Li, Tian & Wei, Yi-ming & Fan, Tijun, 2019. "The VEC-NAR model for short-term forecasting of oil prices," Energy Economics, Elsevier, vol. 78(C), pages 656-667.
    13. Jaehyung An & Alexey Mikhaylov & Nikita Moiseev, 2019. "Oil Price Predictors: Machine Learning Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 9(5), pages 1-6.
    14. repec:hum:wpaper:sfb649dp2014-063 is not listed on IDEAS

    More about this item

    JEL classification:

    • F0 - International Economics - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:mib:wpaper:223. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Matteo Pelagatti (email available below). General contact details of provider: https://edirc.repec.org/data/dpmibit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.