Gaussian Process Regression for Forecasting Gasoline Prices in Jordan

Sameh Asim Ajlouni and Moh'd Taleb Alodat
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Sameh Asim Ajlouni: Faculty of Economics and Administrative Sciences, Department of Economics, Yarmouk University, Irbid, Jordan,
Moh'd Taleb Alodat: Faculty of Science, Department of Statistics, Sultan Qaboos University, Muscat, Sultanate of Oman.

International Journal of Energy Economics and Policy, 2021, vol. 11, issue 3, 502-509

Abstract: The purpose of this paper is to forecast monthly gasoline prices in Jordan by applying Gaussian process regression on monthly prices of two types of gasoline (octane-90 and octane-95) during the period January 2008 to December 2019. Accurately predicting gasoline prices have several fiscal policy implications concerning fuel subsidies and taxes. Also, they affect the consumption and the production of decisions. Moreover, they are crucial for designing and analyzing environmental policies. The Gaussian process model was able to treat a geometric Brownian motion with a deterministic unknown drift function. The performance of prediction was measured using the Root Mean Square Error (RMSE) and the Mean Average Percentage Error (MAPE). Where the numerical results show that the model predictions of gasoline prices were accurate.

Keywords: Geometric Brownian motion; Gaussian process regression; Gasoline prices; Maximum likelihood method; Covariance function (search for similar items in EconPapers)
JEL-codes: C11 C15 Q47 Q48 (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eco:journ2:2021-03-61

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