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Improving oil price forecasts by sparse VAR methods

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  • Krüger, Jens
  • Ruths Sion, Sebastian

Abstract

In this paper we document the results of a forecast evaluation exercise for the real world price of crude oil using VAR models estimated by sparse (regularization) estimators. These methods have the property to constrain single parameters to zero. We find that estimating VARs with three core variables (real price of oil, index of global real economic activity, change in global crude oil production) by the sparse methods is associated with substantial reductions of forecast errors. The transformation of the variables (taking logs or differences) is also crucial. Extending the VARs by further variables is not associated with additonal gains in forecast performance as is the application of impulse indicator saturation before the estimation.

Suggested Citation

  • Krüger, Jens & Ruths Sion, Sebastian, 2019. "Improving oil price forecasts by sparse VAR methods," Darmstadt Discussion Papers in Economics 237, Darmstadt University of Technology, Department of Law and Economics.
    • Handle: RePEc:zbw:darddp:237
    DOI: 10.25534/tuprints-00009643
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    More about this item

    Keywords

    oil price prediction; vector autoregression; regularization;
    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
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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