Improving oil price forecasts by sparse VAR methods

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Darmstadt Discussion Papers in Economics ; No. 237

Classification and Topics

• Classification: Wirtschaft ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Energy Forecasting
• Subject: oil price prediction ; vector autoregression ; regularization

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Krüger, Jens ; Ruths Sion, Sebastian
• Event: Veröffentlichung
• (who): Technische Universität Darmstadt, Department of Law and Economics
• (where): Darmstadt
• (when): 2019

Further information

• DOI: doi:10.25534/tuprints-00009643
• Handle: http://hdl.handle.net/10419/214627
• URN: urn:nbn:de:tuda-tuprints-96436
• Last update: 10.03.2025, 11:44 AM CET