Dynamic Modeling and Forecasting Data Energy Used and Carbon Dioxide (CO2)

Edwin Russel, Wamiliana, Nairobi Saibi, Warsono, Mustofa Usman, Jamal I. Daoud


The model of Vector Autoregressive (VAR) with cointegration is able to be modified by Vector Error Correction Model (VECM). Because of its simpilicity and less restrictions the VECM is applied in many studies. The correlation among variables of multivariate time series also can be explained by VECM model, which can explain the effect of a variable or set of variables on others using Granger Causality, Impulse Response Function (IRF), and Forecasting. In this study, the relationship of Energy Used and CO2 will be discussed. The data used here were collected over the year 1971 to 2018. Based on the comparison of some criteria: Akaike Information Criterion Corrected (AICC), Hannan-Quin Information Criterion (HQC), Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion (SBC) for some VAR(p) model with p= 1,2,3,4,5, the best model with smallest values of AICC, HQC, AIC and SBC is at lag 2 (p= 2). Then the best model found is VECM (2) and further analysis such as Granger Causality, IRF, and Forecasting will be based on this model.


Abolhosseini, S., Heshmati, A., Altmann, J. (2014), The Effect of Renewable Energy Development on
Carbon Emission Reduction: An Empirical Analysis for the EU-15 Countries. IZA, Forschungsinstitut zur Zukunft der Arbeit, Paper No. 7989.

Akpan, G.E., Akpan, U.F. (2012), Electricity consumption, carbon emissions and economic growth in Nigeria. International Journal of Energy Economics and Policy, 2(4), 292-306.

Anjana Das and Kandpal, T.C., (1997).Iron And Steel Anufacturingtechnologiesin India: Estimation of CO2 Emission, International Journal of Energy Research, 21, 1187—1201. https://doi.org/10.1002/(SICI)1099-114X(19971010)21:12<1187::AID-ER320>3.0.CO;2-Y

Asteriou, D. and Hall, S.G.(2007). Applied Econometrics: A Modern Approach. Revised Edition. New York: Palgrave Macmillan.

Azlina, A., Taib, C. (2019), The ımpact of renewable energy consumption on carbon dioxide emissions: Empirical evidence from developing countries in Asia. International Journal of Energy Economics and Policy, 9(3), 135-143. DOI: 10.32479/ijeep.7535.

Bah, M.M., Azam, M. (2017), Investigating the relationship between electricity consumption and economic growth: Evidence from South Africa. Renewable and Sustainable Energy Reviews, 80, 531-537. DOI: 10.1016/j.rser.2017.05.251

Balogh, J.M., Jámbor, A. (2017), Determinants of CO2 emission: A global evidence. International Journal of Energy Economics and Policy, 7(5), 217-226.

Benedetti, M., Bertini, I., Bonfà, F., Ferrari, S., Introna, V., Santino, D., Ubertini, S. (2017), Assessing and improving compressed air systems’ energy efficiency in production and use: Findings from an explorative study in large and energy-intensive industrial firms. Energy Procedia, 105, 3112-3117. DOI: 10.1016/j.egypro.2017.03.653

Brockwell, P.J., and Davis, R.A. (2002). Introduction to Time Series and Forecasting. New York:Springer-Verlag.

Burke, S.P. and Hunter, J. (2005). Modelling Non-Stationary Time Series: A Multivariate Approach, New York: Palgrave Macmillan.

Campiche,J.L., Bryant,H.L., Richardson,J.W., Outlaw,J.L.(2007) Examining the evolving correspondence between petroleum prices and agricultural commodity prices. The American Agricultural Economics Association Annual Meeting, Portland, OR, July 29-August 1, 2007.

Cuthbertson, K., Hall, S.G., Taylor, M.P. (1992). Applied Econometric Techniques, Ann Arbor: The University of Michigan Press.

Di Lorenzo, G., Barbera, P., Ruggieri, G., Witton, J., Pilidis, P., and Probert, D., (2013). Pre-combustion carbon-capture technologies for power generation: an engineering-economic assessment, International Journal of Energy Research. 2013; 37:389–402. doi: 10.1002/er.3029

Engle, R. F. and Yoo, S. (1987), Forecasting and testing in cointegrated systems, Journal of Econometrics 35, 143-159. DOI: https://doi.org/10.1017/CBO9780511599286.008

Engle, R.F. and Granger, C.W.J(1987). Cointegration and error corrections representation, estimation and testing, Econometrica, 55, 251-276. DOI: 10.2307/1913236.

EPA. (2017), Year in Review 2017-2018. Available from: https:// www.epa.gov/sites/production/files/2018-03/documents/year_in_ review_3.5.18.pdf.

Eichler, M., Dahlhaus, R., and Dueck, J. (2017). Graphical modeling for multivariate Hawkes processes with nonparametric link functions. Journal of Time Series Analysis 38: 225–242. DOI: 10.1111/jtsa.12213.

Faizah, S.I., Husaeni, U.A. (2018), Development of consumption and supplying energy in Indonesia’s economy. International Journal of Energy Economics and Policy, 8(6), 313-321. DOI: https://doi.org/10.32479/ijeep.6926

Forero, J.D., Hernández, B. , Orozco, W. , Acuña, N., Wilches, M.J., (2019). Analysis of the use of Renewable Energies in Colombia and the Potential Application of Thermoelectric Devices for Energy Recovery, International Journal of Energy Economics and Policy, 9(5), 125-134. DOI: https://doi.org/10.32479/ijeep.8038.

Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods, Econometrica 37: 424–438. DOI: 10.2307/1912791

Granger, C.W.J.(1981).Some properties of time series data and their use in econometric model specification, Journal of Econometrics 16: 121–130. https://doi.org/10.1017/ccol052179207x.007

Granger, C.W.J.(1983). Cointegrated variables and error correcting models, UCSD Discussion Paper.

Hamilton, J.D., (1994). Time series Analysis. New Jersey: Princeton University Press.

Hatanaka,M.(1996).Time-Series-Based Econometrics:Unit Roots and Co-Integration, Oxford University Press, Oxford.

Hendry, D. F. (1995). Dynamic Econometrics, Oxford University Press, Oxford.

Hunter, J. (1992). Tests of cointegrating exogeneity for PPP and uncovered interest rate parity for the UK. Journal of Policy Modelling, Special Issue: Cointegration, Exogeneity and Policy Analysis 14, 4, 453–63. https://doi.org/10.1016/0161-8938(92)90016-6

Hunter, J and Simpson M. (1995) Exogeneity and identification in a model of the UK effective exchange rate. Paper presented at the EC2 Conference in Aarhus Dec. 1995 and the Econometrics Society European Meeting in Istanbul 1996.

Hunter, J. Burke, S.P., Canepa,A. (2017). Multivariate modelling of Non-Stationary Economic Time Series. London,UK: Palgrave macmillan.

International Energy Agency(IEA), (2007). World Energy Outlook 2007. OECD/IEA: Paris, 2007.

IPCC. (2014), Climate Change: Mitigation of Climate Change. Cambridge, United Kingdom, New York, USA: Cambridge University Press. Available from: http://www. ipcc.ch/report/ar5/wg3.

Jarque, C. M. & Bera, A. K. (1987). A test for normality of observations and regression residuals, International Statistical Review 55: 163–172. DOI: 10.2307/1403192

Johansen, S. (1988). Statistical analysis of cointegration vectors, Journal of Economic Dynamics and Control 12: 231–254. DOI: 10.1016/0165-1889(88)90041-3

Johansen, S. and Juselius, K. (1992). Some structural hypotheses in a multi-variate cointegration analysis of the purchasing power parity and the uncovered interest parity for UK. Journal of Econometrics, 53, 211–44.

Johansen, S. (1995). Likelihood-based inference in cointegrated vector autoregressive models. New York: Oxford University Press.

Juselius, K. (2006) The Cointegrated VAR Model: Econometric Methodology and Empirical Applications, Oxford: Oxford University Press.

Kirchgassner, G. and Wolters, J. (2007). Introduction to Modern Time Series Analysis. Springer, Berlin.

Lamees El Araby, Nagwa Samak, Dalia M. Ibrahiem (2019). The Impact of Renewable and Non-renewable Energy on Carbon Dioxide Emission: An Empirical Analysis for Euro Mediterranean Countries, International Journal of Energy Economics and Policy, 2019, 9(6), 103-108. DOI: https://doi.org/10.32479/ijeep.8254

Lutkepohl, H. and Kratzig, M. (2004). Applied Time series Econometrics, New York: Cambridge University Press.

Lutkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Berlin :Springer Verlaag.

Lutkepohl, H. (2011). Vector Autoregressive Models. EUI Working Paper ECO 2011/30, Department of Economics, European University Institute, Florence.

Montgomery, D.C., Jennings, C.L., and Kulahci, M. (2008). Introduction Time Series Analysis and Forecasting. New Jersey. John Wiley & Sons, Inc.

Mbanda L. N., and Zhongqun Wu. (2019), Jean Gaston Tamba3Empirical Analysis of Electricity Consumption, CO2 Emissions and Economic Growth: Evidence from Cameroon, International Journal of Energy Economics and Policy, 2019, 9(5), 63-73. DOI: https://doi.org/10.32479/ijeep.7915

OECD. 1995. OECD Environmental Data Compendium 1995. OECD: Paris.

Pala, Aynur (2013). Structural Breaks, Cointegration, and Causality by VECM Analysis of Crude Oil and Food Price. International Journal of Energy Economics and Policy. 3(3); 238-246.

Rachev, S.T., Mittnik, S., Fabozzi, F.J., Focardi, S.M., and Jasic, T.,(2007). Financial Econometrics: From Basics to Advanced Modeling Techniques, New Jersey: John Wiley & Sons, Inc.

Sakamoto,Y., and Y. Tonooka, (2000). Estimation of CO2 emission for each process in the Japanese steel industry: a process analysis, International Journal of Energy Research, 24:625-632.
DOI: 10.1002/1099-114x(20000610)24:7<625::aid-er616>3.0.co;2-r

Sampson, Michael.(2001). Time Series Analysis. Montreal, Canada:Loglinear Publications.

SAS/ETS 13.2 (2014). User Guide The VARMAX Procedure. Cary, North Carolina: SAS Institute Inc., SAS Campus Drive.

Sims, C.A., (1980). Macroeconomics and Reality. Econometrica, 48, 11-48. DOI: 10.2307/1912017

Tsay, R.S. (2005). Analysis of Financial Time series, 2nd edition, Hoboken, New Jersey: John Wiley and Sons, Inc.

Tsay, R.S. (2014). Multivariate Time series Analysis: with R and Financial Applications, Hoboken, New Jersey: John Wiley and Sons, Inc.

UN UNCED. 1992. UN Framework Convention on Climate Change (UNFCCC). Rio de Janeiro, Brazil, 3-4 June.

UN UNCED. 1996. UN Framework Convention on Climate Change (UNFCCC), Conference of ¹he Parties 2 (COP2), Geneva, Switzerland, 8-19 July.

Wang Yu, Guo Ju’e, Xi Youmin.(2008). Study on the dynamic Relationship between Economic Growth and China Energy Based on Cointegration Analysis and Impulse Response Function. China Population Resources and Environment, 18(4):056-061. DOI: 10.1016/s1872-583x(09)60013-9

Warsono, Edwin Russel, Wamiliana, Widiarti, Mustofa Usman. (2019a). Vector Autoregressive with Exogenous Variable Model and its Application in Modeling and Forecasting Energy Data: Case Study of PTBA and HRUM Energy. International Journal of Energy Economics and Policy. 9(2); 390-398. DOI: https://doi.org/10.32479/ijeep.7223

Warsono, Edwin Russel, Wamiliana,Widiarti, Mustofa Usman.(2019b). Modeling and Forecasting by the Vector Autoregressive Moving Average Model for Export of Coal and Oil Data (Case Study from Indonesia over the Years 2002-2017). International Journal of Energy Economics and Policy, 9(4); 240-247. DOI:https://doi.org/10.32479/ijeep.7605

Wei, William W. S. (2006). Time Series Analysis Univariate and Multivariate Methods. 2nd edition. Boston: Pearson Education, Inc.

Wei, William W. S. (2019). Multivariate Time Series Analysis and Applications, Hoboken, New Jersey: John Wiley and Sons, Inc.

Yoo, S. (1986) Multi-cointegrated time series and generalised error-correction models. University of San Diego working paper.

Yu,T-H., Bessler,D.A., Fuller,S.(2006). Cointegration and Causality Analysis of World Vegetable Oil and Crude Oil Prices, American Agricultural Economics Association Annual Meeting, Long Beach, CA, July 23-26.


Edwin Russel
wamiliana.1963@fmipa.unila.ac.id (Primary Contact)
Nairobi Saibi
Mustofa Usman
Jamal I. Daoud
Russel, E., Wamiliana, Saibi, N., Warsono, Usman, M., & Daoud, J. I. (2022). Dynamic Modeling and Forecasting Data Energy Used and Carbon Dioxide (CO2). Science and Technology Indonesia, 7(2), 228–237. https://doi.org/10.26554/sti.2022.7.2.228-237

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