期刊名称：International Journal of Energy Economics and Policy
电子版ISSN：2146-4553
出版年度：2015
卷号：5
期号：3
页码：716-724
语种：English
出版社：EconJournals
摘要：For a sustainable economic development, premium fuel forecasting is becoming increasingly relevant to policy makers and consumers. The current paper develops a structural econometric model of premium fuel using the Autoregressive Integrated Moving Average (ARIMA) to analyse and forecast premium demand. The results show that the ARIMA models (1, 1, 0); (0, 1, 1) and (1, 1, 1) are the appropriate identified order. The estimated models included a constant term. All the coefficients of the variables in the model except the constant term were significant. The diagnostic checking of the estimated model shows ARIMA (1, 1, 1) as the best fitted model since all the series were randomly distributed. The data for the forecast covers the period 2000:01 to 2011:12. The results indicated that the forecasted values fitted the actual consumption of the energy variables since the forecasted values insignificantly underestimate the actual consumption and thus indicate consistency of the results. The evaluation statistics indicate that the estimated models are suitable for forecasting. The model developed in the work is helpful to the energy sector and policy makers in making energy related decisions and investigating the changes in premium demand. Keywords: Premium fuel; ARIMA; Forecasting JEL Classifications: C51; C52; C53; E17; Q47
其他摘要：For a sustainable economic development, premium fuel forecasting is becoming increasingly relevant to policy makers and consumers. The current paper develops a structural econometric model of premium fuel using the Autoregressive Integrated Moving Average (ARIMA) to analyse and forecast premium demand. The results show that the ARIMA models (1, 1, 0); (0, 1, 1) and (1, 1, 1) are the appropriate identified order. The estimated models included a constant term. All the coefficients of the variables in the model except the constant term were significant. The diagnostic checking of the estimated model shows ARIMA (1, 1, 1) as the best fitted model since all the series were randomly distributed. The data for the forecast covers the period 2000:01 to 2011:12. The results indicated that the forecasted values fitted the actual consumption of the energy variables since the forecasted values insignificantly underestimate the actual consumption and thus indicate consistency of the results. The evaluation statistics indicate that the estimated models are suitable for forecasting. The model developed in the work is helpful to the energy sector and policy makers in making energy related decisions and investigating the changes in premium demand. Keywords: Premium fuel; ARIMA; Forecasting JEL Classifications: C51; C52; C53; E17; Q47
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