 Predicting oil price movements: A dynamic Artificial Neural Network approachAli Abbasi Godarzi, Rohollah Madadi Amiri, Alireza Talaei and Tooraj Jamasb Energy Policy, 2014, vol. 68, issue C, 371-382 Abstract: Price of oil is important for the economies of oil exporting and oil importing countries alike. Therefore, insight into the likely future behaviour and patterns of oil prices can improve economic planning and reduce the impacts of oil market fluctuations. This paper aims to improve the application of Artificial Neural Network (ANN) techniques to prediction of oil price. We develop a dynamic Nonlinear Auto Regressive model with eXogenous input (NARX) as a form of ANN to account for the time factor. We estimate the model using macroeconomic data from OECD countries. In order to compare the results, we develop time series and ANN static models. We then use the output of time series model to develop a NARX model. The NARX model is trained with historical data from 1974 to 2004 and the results are verified with data from 2005 to 2009. The results show that NARX model is more accurate than time series and static ANN models in predicting oil prices in general as well as in predicting the occurrence of oil price shocks. Keywords: Oil price forecasting; Time series model; NARX model (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:enepol:v:68:y:2014:i:c:p:371-382 DOI: 10.1016/j.enpol.2013.12.049 Access Statistics for this article Energy Policy is currently edited by N. France More articles in Energy Policy from Elsevier |
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