Application of econometrics in energy research—Empowerment from big data and machine learning

Abstract

AbstractThis article expounds on the challenges of sustainable global energy development to conventional econometrics in energy research. It introduces energy big data's “4V” characteristics, that is, volume, variety, velocity, and veracity. Further, the article presents a series of challenges the “4V” feature brings to conventional econometrics research on energy issues. Then, by reviewing the existing literature, the article proposes the improvement and promotion of conventional econometrics in the three directions of data exploration and variable generation, prediction, and causal inference of machine learning in big data. Finally, the article concludes that the discipline of econometrics will never go out of fashion. Although big data and machine learning will increasingly challenge the conventional econometrics research paradigm, econometrics will be reborn in the way of “phoenix nirvana”.This article is categorized under: Emerging Technologies > Digitalization