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Forecasting U.S. real GDP using oil prices: A time-varying parameter MIDAS model

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  • Pan, Zhiyuan
  • Wang, Qing
  • Wang, Yudong
  • Yang, Li

Abstract

In this paper, we introduce the functional coefficient to existing mixed-frequency data sampling (MIDAS) regression to make the parameter change over time. The proposed time-varying parameter MIDAS (TVP-MIDAS) is employed to forecast the U.S. real GDP growth using crude oil prices. We find the out-of-sample predictability of GDP growth across different forecasting horizons. The percent reduction of mean squared predictive error achieves 14% when the nonlinear oil price measure is employed. The TVP-MIDAS can outperform a series of competing models including the OLS regression with quarterly oil price, the constant coefficient and Markov regime switching MIDAS regressions.

Suggested Citation

  • Pan, Zhiyuan & Wang, Qing & Wang, Yudong & Yang, Li, 2018. "Forecasting U.S. real GDP using oil prices: A time-varying parameter MIDAS model," Energy Economics, Elsevier, vol. 72(C), pages 177-187.
    • Handle: RePEc:eee:eneeco:v:72:y:2018:i:c:p:177-187
    DOI: 10.1016/j.eneco.2018.04.008
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    Cited by:

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    2. Zhang, Yue-Jun & Wang, Jin-Li, 2019. "Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models," Energy Economics, Elsevier, vol. 78(C), pages 192-201.
    3. Lin, Boqiang & Xu, Bin, 2019. "How to effectively stabilize China's commodity price fluctuations?," Energy Economics, Elsevier, vol. 84(C).
    4. Ye, Wuyi & Guo, Ranran & Deschamps, Bruno & Jiang, Ying & Liu, Xiaoquan, 2021. "Macroeconomic forecasts and commodity futures volatility," Economic Modelling, Elsevier, vol. 94(C), pages 981-994.
    5. Yin, Libo & Feng, Jiabao & Liu, Li & Wang, Yudong, 2019. "It's not that important: The negligible effect of oil market uncertainty," International Review of Economics & Finance, Elsevier, vol. 60(C), pages 62-84.
    6. Zhang, Zhikai & He, Mengxi & Zhang, Yaojie & Wang, Yudong, 2021. "Realized skewness and the short-term predictability for aggregate stock market volatility," Economic Modelling, Elsevier, vol. 103(C).
    7. Philip ME Garboden, 2019. "Sources and Types of Big Data for Macroeconomic Forecasting," Working Papers 2019-3, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    8. Fu, Zhonghao & Hong, Yongmiao & Su, Liangjun & Wang, Xia, 2023. "Specification tests for time-varying coefficient models," Journal of Econometrics, Elsevier, vol. 235(2), pages 720-744.
    9. Yang, Lu & Cui, Xue & Yang, Lei & Hamori, Shigeyuki & Cai, Xiaojing, 2023. "Risk spillover from international financial markets and China's macro-economy: A MIDAS-CoVaR-QR model," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 55-69.
    10. Ding, Lili & Zhao, Zhongchao & Han, Meng, 2021. "Probability density forecasts for steam coal prices in China: The role of high-frequency factors," Energy, Elsevier, vol. 220(C).

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    More about this item

    Keywords

    Functional coefficient; Mixed-frequency data sampling; Crude oil price; Real GDP growth; Forecasting;
    All these keywords.

    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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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