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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.

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  • Krüger, Jens & Ruths Sion, Sebastian, 2019. "Improving Oil Price Forecasts by Sparse VAR Methods," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 118208, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    • Handle: RePEc:dar:wpaper:118208
    Note: for complete metadata visit http://tubiblio.ulb.tu-darmstadt.de/118208/
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    Cited by:

    1. Xing, Li-Min & Zhang, Yue-Jun, 2022. "Forecasting crude oil prices with shrinkage methods: Can nonconvex penalty and Huber loss help?," Energy Economics, Elsevier, vol. 110(C).

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