文章基本信息

标题：Forecasting European Union CO2 Emissions Using Autoregressive Integrated Moving Average-autoregressive Conditional Heteroscedasticity Models
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作者：Melina Dritsaki ; Chaido Dritsaki
期刊名称：International Journal of Energy Economics and Policy
电子版ISSN：2146-4553
出版年度：2020
卷号：10
期号：4
页码：411-423
DOI：10.32479/ijeep.9186
出版社：EconJournals
摘要：In the past few decades, there are lot of discussions around global warming and climate change primarily due to the increased CO2 emissions generated by the consumption of fossil fuels, such as oil and natural gas. After an enormous effort, the EU-28 managed to reduce CO2 emissions in 2014 by 25.7% comparing to 1990 (Kyoto Protocol). This effort should continue in the future so that the EU-28 achieve a 40% reduction on CO2 emissions by 2030. The current paper aims at investigating the optimum model to forecast CO2 emissions in the EU-28. To achieve this aim an ARIMA(1,1,1)-ARCH(1) model was used, combined with the linear ARIMA model and the conditional variance of the ARCH model. The estimation of parameter optimisation of ARIMA(1,1,1)-ARCH(1) model was done with the Maximum Likelihood approach using the Marquardt (1963), and Berndt-Hall-Hall-Hausman (BHHH) algorithms and the three distributions (Normal, t-Student, Generalized error), whereas for the estimation of the covariance coefficient the reversed matrix by Hessian was used. Finally, in order to forecast the ARIMA(1,1,1)-ARCH(1) model, a dynamic as well as a static process was applied. The results of the forecasting revealed that the static procedure provides a better forecast comparing to the dynamic one.
关键词：CO2 Emissions;ARIMA(1;1;1)-ARCH(1) model;Forecasting;E.U