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What if Energy Time Series are not Independent? Implications for Energy-GDP Causality Analysis

Author

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  • Bruns, Stephan B.

    (Max Planck Institute of Economics)

  • Gross, Christian

    (E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN))

Abstract
Time series of electricity, petroleum products, and renewables are found to be highly correlated with total energy consumption. Applying this insight to the huge literature on energy-GDP causality explains that the results of energy-GDP causality tests frequently coincide with the results of energy type-GDP tests. Using the test by Toda-Yamamoto in combination with a cointegration-based testing approach, we detect such cases of concordance for 92 per cent of the countries in our sample of 65 countries. As a consequence, it is difficult to draw specific economic conclusions regarding single types of energy from bivariate causality analysis.

Suggested Citation

  • Bruns, Stephan B. & Gross, Christian, 2013. "What if Energy Time Series are not Independent? Implications for Energy-GDP Causality Analysis," FCN Working Papers 10/2013, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    • Handle: RePEc:ris:fcnwpa:2013_010
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    3. Hasanov, Fakhri & Bulut, Cihan & Suleymanov, Elchin, 2017. "Review of energy-growth nexus: A panel analysis for ten Eurasian oil exporting countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 369-386.
    4. Rohlfs, Wilko & Madlener, Reinhard, 2013. "Challenges in the Evaluation of Ultra-Long-Lived Projects: Risk Premia for Projects with Eternal Returns or Costs," FCN Working Papers 13/2013, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    5. Stephan B. Bruns, Christian Gross and David I. Stern, 2014. "Is There Really Granger Causality Between Energy Use and Output?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
    6. Nurcan Kilinc-Ata, 2018. "Assessing the Future of Renewable Energy Consumption for United Kingdom, Turkey and Nigeria," Foresight and STI Governance (Foresight-Russia till No. 3/2015), National Research University Higher School of Economics, vol. 12(4), pages 62-77.
    7. Hasanov, Akram Shavkatovich & Do, Hung Xuan & Shaiban, Mohammed Sharaf, 2016. "Fossil fuel price uncertainty and feedstock edible oil prices: Evidence from MGARCH-M and VIRF analysis," Energy Economics, Elsevier, vol. 57(C), pages 16-27.
    8. Soytas, Ugur & Magazzino, Cosimo & Mele, Marco & Schneider, Nicolas, 2022. "Economic and environmental implications of the nuclear power phase-out in Belgium: Insights from time-series models and a partial differential equations algorithm," Structural Change and Economic Dynamics, Elsevier, vol. 63(C), pages 241-256.
    9. Bennouna, Amin & El Hebil, Charaf, 2016. "Energy needs for Morocco 2030, as obtained from GDP-energy and GDP-energy intensity correlations," Energy Policy, Elsevier, vol. 88(C), pages 45-55.
    10. Paresh Narayan & Russell Smyth, 2014. "Applied Econometrics and a Decade of Energy Economics Research," Monash Economics Working Papers 21-14, Monash University, Department of Economics.
    11. Ikegami, Masako & Wang, Zijian, 2016. "The long-run causal relationship between electricity consumption and real GDP: Evidence from Japan and Germany," Journal of Policy Modeling, Elsevier, vol. 38(5), pages 767-784.
    12. Dutta, Champa Bati & Das, Debasish Kumar, 2016. "Does disaggregated CO2 emission matter for growth? Evidence from thirty countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 825-833.
    13. Syed Zwick, Hélène & Syed, Sarfaraz Ali Shah & Liddle, Brantley & Lung, Sidney, 2017. "Disaggregated relationship between economic growth and energy use in OECD countries: Time-series and cross-country evidence," MPRA Paper 93271, University Library of Munich, Germany.

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    More about this item

    Keywords

    Energy; GDP; Granger causality; Correlation; Electricity; Petroleum products; Renewables; Toda-Yamamoto; Johansen-Juselius;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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