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Forecasting crude oil prices with DSGE models

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  • Michał Rubaszek

    (SGH Warsaw School of Economics)

Abstract

We run an oil prices forecasting competition among a set of structural models, includ- ing vector autoregressions and dynamic stochastic general equilibrium models. Our results highlights two principles. First, forecasts should exploit the mean reversion of the real oil price over long horizons. Second, models should not replicate the high volatility of oil prices observed in sample. Abiding by these principles, we show that a small scale DSGE model performs much better in real oil price forecasting than the random walk as well as vector autoregressions.

Suggested Citation

  • Michał Rubaszek, 2019. "Forecasting crude oil prices with DSGE models," GRU Working Paper Series GRU_2019_024, City University of Hong Kong, Department of Economics and Finance, Global Research Unit.
    • Handle: RePEc:cth:wpaper:gru_2019_024
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    More about this item

    Keywords

    Forecasting; oil prices; DSGE models; vector autoregression; Bayesian inference;
    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
    • Q35 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - Hydrocarbon Resources
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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