The Dynamics of US Gasoline Demand and Its Prediction: An Extended Dynamic Model Averaging Approach

Sakar Hasan Hamza () and Qingna Li
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Sakar Hasan Hamza: School of Mathematics and Statistics, Beijing Institute of Technology, Beijing 100081, China
Qingna Li: School of Mathematics and Statistics, Beijing Institute of Technology, Beijing 100081, China

Energies, 2023, vol. 16, issue 12, 1-13

Abstract: This study contributes to the body of literature on modeling and predicting gasoline demand by using nonlinear econometric techniques. For this purpose, dynamic model averaging (DMA) and Bayesian model averaging (BMA) combined with Artificial Bee Colony (ABC) are used to forecast gasoline consumption in the United States. The article’s independent variables include demographic characteristics, economic activity, income, driving expenditures, automobile price, and road availability for annual data from 1960 to 2020. In the proposed model, not only may the coefficients and elasticity of a predictor of gasoline demand change over time, but other sets of predictors can also emerge at different periods. Moreover, this study aims to automate the process of picking two forgotten variables of the DMA model using the ABC model. Our findings indicate that dynamic model averaging significantly improves forecasting performance when compared to basic benchmark techniques and advanced approaches. Additionally, integrating it with an Artificial Bee Colony (ABC) may result in improved outcomes when time-varying forgetting variables are present. The findings of this research provide policymakers in the fields of energy economics and the environment with helpful tools and information.

Keywords: gasoline demand; dynamic model averaging (DMA); artificial bee colony (ABC); time-varying parameter; dynamic model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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