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Forecasting GDP with Oil Price Shocks: A Mixed-Frequency Time-Varying Perspective

Author

Listed:
  • Jiawen Luo

    (School of Business Administration, South China University of Technology, Guangzhou 510640, China)

  • Jingyi Deng

    (School of Business Administration, South China University of Technology, Guangzhou 510640, China)

  • Juncal Cunado

    (University of Navarra, School of Economics, Edificio Amigos, E-31080 Pamplona, Spain)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

Abstract

This paper investigates the predictability of supply and demand oil price shocks on U.S. Gross Domestic Product (GDP) using several Mixed Data Sampling (MIDAS) models that link quarterly GDP to monthly oil price shocks for the period 1981-2023. The main findings reveal that oil demand shocks, particularly economic activity and inventory shocks, have higher forecast ability than oil supply shocks, highlighting the importance of disentangling oil price shocks into their underlying components. Additionally, our results suggest that the Time Varying Parameter (TVP)-MIDAS model most effectively captures the dynamic relationship between oil price fluctuations and economic activity, pointing to the heterogeneous impact of oil price shocks over time. Finally, when we extend our analysis to other regions in the world, the results suggest that while oil demand shocks play a significant role in forecasting economic activity in advanced regions, the emerging regions are more vulnerable to oil supply shocks.

Suggested Citation

  • Jiawen Luo & Jingyi Deng & Juncal Cunado & Rangan Gupta, 2025. "Forecasting GDP with Oil Price Shocks: A Mixed-Frequency Time-Varying Perspective," Working Papers 202523, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202523
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    Keywords

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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