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

Jiawen Luo (), Jingyi Deng (), Juncal Cunado () and Rangan Gupta ()
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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

No 202523, Working Papers from University of Pretoria, Department of Economics

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.

Keywords: Oil price shocks; economic activity; Mixed-Data-Sampling (MIDAS); Time-Varying Parameter MIDAS (TVP-MIDAS); Forecast evaluation (search for similar items in EconPapers)
JEL-codes: C22 C53 Q41 (search for similar items in EconPapers)
Pages: 62 pages
Date: 2025-07
New Economics Papers: this item is included in nep-ets and nep-for
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