An alternative reference scenario for global CO2 emissions from fuel consumption: An ARFIMA approach
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- José M. Belbute & Alfredo Marvão Pereira, 2015. "An Alternative Reference Scenario for Global CO2Emissions from Fuel Consumption: An ARFIMA Approach," Working Papers 164, Department of Economics, College of William and Mary.
- José Belbute & Alfredo M. Pereira, 2015. "An Alternative Reference Scenario for Global CO2Emissions from Fuel Consumption: An ARFIMA Approach," CEFAGE-UE Working Papers 2015_11, University of Evora, CEFAGE-UE (Portugal).
References listed on IDEAS
- Gil-Alana, Luis A. & Loomis, David & Payne, James E., 2010.
"Does energy consumption by the US electric power sector exhibit long memory behavior?,"
Elsevier, vol. 38(11), pages 7512-7518, November.
- Luis A. Gil-Alana & James Payne & David Loomis, 2010. "Does energy consumption by the US electric power secto exhibit long memory behaviour?," Faculty Working Papers 04/10, School of Economics and Business Administration, University of Navarra.
- Elder, John & Serletis, Apostolos, 2008. "Long memory in energy futures prices," Review of Financial Economics, Elsevier, vol. 17(2), pages 146-155.
- Marco Barassi & Matthew Cole & Robert Elliott, 2011.
"The Stochastic Convergence of CO 2 Emissions: A Long Memory Approach,"
Environmental & Resource Economics,
Springer;European Association of Environmental and Resource Economists, vol. 49(3), pages 367-385, July.
- Marco R Barassi & Matthew A Cole & Robert J R Elliott, 2010. "The Stochastic Convergence of CO2 Emissions: A Long Memory Approach," Discussion Papers 10-32, Department of Economics, University of Birmingham.
- Lean, Hooi Hooi & Smyth, Russell, 2009. "Long memory in US disaggregated petroleum consumption: Evidence from univariate and multivariate LM tests for fractional integration," Energy Policy, Elsevier, vol. 37(8), pages 3205-3211, August.
- Pestana Barros, Carlos & Gil-Alana, Luis A. & Payne, James E., 2012. "Evidence of long memory behavior in U.S. renewable energy consumption," Energy Policy, Elsevier, vol. 41(C), pages 822-826.
- Apergis, Nicholas & Tsoumas, Chris, 2012. "Long memory and disaggregated energy consumption: Evidence from fossils, coal and electricity retail in the U.S," Energy Economics, Elsevier, vol. 34(4), pages 1082-1087.
- Liu, Hsiang-Hsi & Chen, Yi-Chun, 2013. "A study on the volatility spillovers, long memory effects and interactions between carbon and energy markets: The impacts of extreme weather," Economic Modelling, Elsevier, vol. 35(C), pages 840-855.
- Apergis, Nicholas & Tsoumas, Chris, 2011. "Integration properties of disaggregated solar, geothermal and biomass energy consumption in the U.S," Energy Policy, Elsevier, vol. 39(9), pages 5474-5479, September.
CitationsCitations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
- José Belbute & Alberto Marvão Pereira, 2015. "Do Global CO2 Emissions from Fuel Consumption Exhibit Long Memory? A Fractional Integration Analysis," CEFAGE-UE Working Papers 2015_14, University of Evora, CEFAGE-UE (Portugal).
- repec:eee:rensus:v:80:y:2017:i:c:p:990-1016 is not listed on IDEAS
- José Belbute & Alfredo M. Pereira, 2016.
"Updated Reference Forecasts for Global CO2 Emissions from Fossil-Fuel Consumption,"
CEFAGE-UE Working Papers
2016_08, University of Evora, CEFAGE-UE (Portugal).
- José M. Belbute & Alfredo Marvão Pereira, 2016. "Updated Reference Forecasts for Global CO2 Emissions from Fossil-Fuel Consumption," Working Papers 170, Department of Economics, College of William and Mary.
More about this item
KeywordsForecasting; Reference scenario; CO2 emissions; Fuel; Long memory; ARFIMA;
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
- Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
- Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
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