An Econometric Investigation of Forecasting Premium Fuel

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 dema...

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Main Authors: Samuel Asuamah Yeboah, Joseph Ohene-Manu
Format: Article
Language:English
Published: EconJournals 2015-09-01
Series:International Journal of Energy Economics and Policy
Subjects:
Online Access:https://dergipark.org.tr/tr/pub/ijeeep/issue/31914/350997?publisher=http-www-cag-edu-tr-ilhan-ozturk
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spelling doaj-db0c41011203448ca8f3530414ed08d52020-11-25T03:44:57ZengEconJournalsInternational Journal of Energy Economics and Policy2146-45532015-09-01537167241032An Econometric Investigation of Forecasting Premium FuelSamuel Asuamah YeboahJoseph Ohene-ManuFor 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.https://dergipark.org.tr/tr/pub/ijeeep/issue/31914/350997?publisher=http-www-cag-edu-tr-ilhan-ozturkpremium fuel arima forecasting
collection DOAJ
language English
format Article
sources DOAJ
author Samuel Asuamah Yeboah
Joseph Ohene-Manu
spellingShingle Samuel Asuamah Yeboah
Joseph Ohene-Manu
An Econometric Investigation of Forecasting Premium Fuel
International Journal of Energy Economics and Policy
premium fuel
arima
forecasting
author_facet Samuel Asuamah Yeboah
Joseph Ohene-Manu
author_sort Samuel Asuamah Yeboah
title An Econometric Investigation of Forecasting Premium Fuel
title_short An Econometric Investigation of Forecasting Premium Fuel
title_full An Econometric Investigation of Forecasting Premium Fuel
title_fullStr An Econometric Investigation of Forecasting Premium Fuel
title_full_unstemmed An Econometric Investigation of Forecasting Premium Fuel
title_sort econometric investigation of forecasting premium fuel
publisher EconJournals
series International Journal of Energy Economics and Policy
issn 2146-4553
publishDate 2015-09-01
description 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.
topic premium fuel
arima
forecasting
url https://dergipark.org.tr/tr/pub/ijeeep/issue/31914/350997?publisher=http-www-cag-edu-tr-ilhan-ozturk
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