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Research on Reconstructing Regional Business Cycle Analysis System Based on Electricity Big Data—A Case Study in Guangxi Province

Author

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  • Zhiwei Cui

    (Guangxi Power Grid Co., Ltd., Nanning 530023, China)

  • Qideng Luo

    (Guangxi Power Grid Co., Ltd., Nanning 530023, China)

  • Haoyang Ji

    (School of Economics, Peking University, Beijing 100871, China
    Carbon Econometric Research Center, Beijing University of Posts and Telecommunications, Beijing 100876, China)

  • Yang Xu

    (Carbon Econometric Research Center, Beijing University of Posts and Telecommunications, Beijing 100876, China)

  • Junyi Shi

    (Carbon Econometric Research Center, Beijing University of Posts and Telecommunications, Beijing 100876, China
    School of Statistics, Beijing Normal University, Beijing 100875, China)

Abstract

Existing systems for analyzing regional business cycles mostly select indicators from the macro perspective of consumption, investment, employment, etc., and use industrial value added or quarterly GDP as the benchmark cycle indicator. In order to better construct the benchmark cycle indicators, we introduce the Denton model to convert the quarterly GDP to the monthly GDP and select it as the benchmark cycle indicator. This study reconstructed a regional economic cycle analysis system from the perspective of energy using the power big data of Guangxi from January 2014 to December 2024. It compares results with macro-perspective and combined energy-macro approaches, demonstrating that the electric power big data approach enables timely reconstruction of the analysis system with maintained accuracy, enhancing the system’s timeliness. Therefore, the regional business cycle analysis system based on electric power big data can effectively avoid the problem of lag in the release of a monthly business cycle index and has important reference significance for building a high-quality macro real-time monitoring system.

Suggested Citation

  • Zhiwei Cui & Qideng Luo & Haoyang Ji & Yang Xu & Junyi Shi, 2025. "Research on Reconstructing Regional Business Cycle Analysis System Based on Electricity Big Data—A Case Study in Guangxi Province," Energies, MDPI, vol. 18(11), pages 1-13, June.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:11:p:2921-:d:1670478
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    References listed on IDEAS

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    1. Hunt Allcott & Allan Collard-Wexler & Stephen D. O'Connell, 2016. "How Do Electricity Shortages Affect Industry? Evidence from India," American Economic Review, American Economic Association, vol. 106(3), pages 587-624, March.
    2. Voigt, Sebastian & De Cian, Enrica & Schymura, Michael & Verdolini, Elena, 2014. "Energy intensity developments in 40 major economies: Structural change or technology improvement?," Energy Economics, Elsevier, vol. 41(C), pages 47-62.
    3. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-1069, June.
    4. Hamilton, James D, 1983. "Oil and the Macroeconomy since World War II," Journal of Political Economy, University of Chicago Press, vol. 91(2), pages 228-248, April.
    5. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
    6. Camacho, Maximo & Perez-Quiros, Gabriel & Poncela, Pilar, 2013. "Short-term Forecasting for Empirical Economists: A Survey of the Recently Proposed Algorithms," Foundations and Trends(R) in Econometrics, now publishers, vol. 6(2), pages 101-161, November.
    7. Yuan, Jiahai & Zhao, Changhong & Yu, Shunkun & Hu, Zhaoguang, 2007. "Electricity consumption and economic growth in China: Cointegration and co-feature analysis," Energy Economics, Elsevier, vol. 29(6), pages 1179-1191, November.
    8. James H. Stock & Mark W. Watson, 1988. "A Probability Model of The Coincident Economic Indicators," NBER Working Papers 2772, National Bureau of Economic Research, Inc.
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