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The effect of oil uncertainty shock on real GDP of 33 countries: a global VAR approach

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  • Afees A. Salisu
  • Rangan Gupta
  • Abeeb Olaniran

Abstract

In this paper, we investigate the effect of oil price uncertainty shock on real Gross Domestic Product (GDP) of 33 developed and emerging economies using the Global Vector Autoregressive (VAR) framework that allows us to capture the transmission of global shocks while simultaneously accounting for distinct characteristics of individual countries. Utilizing quarterly data over the period of 1980Q1 to 2019Q2, we show that, in general, oil price uncertainty shock has a statistically significant negative impact on GDP for 28 out of the 33 countries, but with varying magnitude and persistence. Overall though, we find the adverse effect on real GDP to be relatively stronger for the developed group of countries than the emerging ones. Hence, our results suggest that policymakers must be ready to undertake expansionary policies (of varying order) in the wake of an oil price uncertainty shock to prevent deep recessions, except in the cases of Norway, Philippines and Saudi Arabia, for which output tends to increase in a statistically significant manner.

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  • Afees A. Salisu & Rangan Gupta & Abeeb Olaniran, 2023. "The effect of oil uncertainty shock on real GDP of 33 countries: a global VAR approach," Applied Economics Letters, Taylor & Francis Journals, vol. 30(3), pages 269-274, February.
    • Handle: RePEc:taf:apeclt:v:30:y:2023:i:3:p:269-274
    DOI: 10.1080/13504851.2021.1983134
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    Cited by:

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    2. Xin Sheng & Rangan Gupta & Qiang Ji, 2023. "The Effects of Disaggregate Oil Shocks on the Aggregate Expected Skewness of the United States," Risks, MDPI, vol. 11(11), pages 1-9, October.
    3. Salisu, Afees A. & Adediran, Idris & Omoke, Philip C. & Tchankam, Jean Paul, 2023. "Gold and tail risks," Resources Policy, Elsevier, vol. 80(C).
    4. Rangan Gupta & Christian Pierdzioch, 2023. "Do U.S. economic conditions at the state level predict the realized volatility of oil-price returns? A quantile machine-learning approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-22, December.
    5. Zhang, Tianding & Zeng, Song, 2023. "Dynamic comovement and extreme risk spillovers between international crude oil and China's non-ferrous metal futures market," Resources Policy, Elsevier, vol. 80(C).
    6. Salisu, Afees A. & Ogbonna, Ahamuefula E. & Vo, Xuan Vinh, 2023. "Oil tail risks and the realized variance of consumer prices in advanced economies," Resources Policy, Elsevier, vol. 83(C).
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    More about this item

    JEL classification:

    • 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
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market

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