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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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    References listed on IDEAS

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    Citations

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    Cited by:

    1. 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.
    2. 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.

    More about this item

    Keywords

    Functional coefficient; Mixed-frequency data sampling; Crude oil price; Real GDP growth; Forecasting;

    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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