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Real-time estimation of the short-run impact of COVID-19 on economic activity using electricity market data

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  • Carlo Fezzi
  • Valeria Fanghella

Abstract

The COVID-19 pandemic has caused more than 8 million confirmed cases and 500,000 death to date. In response to this emergency, many countries have introduced a series of social-distancing measures including lockdowns and businesses' temporary shutdowns, in an attempt to curb the spread of the infection. Accordingly, the pandemic has been generating unprecedent disruption on practically every aspect of society. This paper demonstrates that high-frequency electricity market data can be used to estimate the causal, short-run impact of COVID-19 on the economy. In the current uncertain economic conditions, timeliness is essential. Unlike official statistics, which are published with a delay of a few months, with our approach one can monitor virtually every day the impact of the containment policies, the extent of the recession and measure whether the monetary and fiscal stimuli introduced to address the crisis are being effective. We illustrate our methodology on daily data for the Italian day-ahead power market. Not surprisingly, we find that the containment measures caused a significant reduction in economic activities and that the GDP at the end of in May 2020 is still about 11% lower that what it would have been without the outbreak.

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  • Carlo Fezzi & Valeria Fanghella, 2020. "Real-time estimation of the short-run impact of COVID-19 on economic activity using electricity market data," Papers 2007.03477, arXiv.org.
    • Handle: RePEc:arx:papers:2007.03477
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    1. Carlo Fezzi & Derek Bunn, 2010. "Structural Analysis of Electricity Demand and Supply Interactions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(6), pages 827-856, December.
    2. Carlo Fezzi and Luca Mosetti, 2020. "Size Matters: Estimation Sample Length and Electricity Price Forecasting Accuracy," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 231-254.
    3. Malinauskaite, J. & Jouhara, H. & Ahmad, L. & Milani, M. & Montorsi, L. & Venturelli, M., 2019. "Energy efficiency in industry: EU and national policies in Italy and the UK," Energy, Elsevier, vol. 172(C), pages 255-269.
    4. Francisco J. Buera & Joseph P. Kaboski, 2012. "The Rise of the Service Economy," American Economic Review, American Economic Association, vol. 102(6), pages 2540-2569, October.
    5. Chen, Sheng-Tung & Kuo, Hsiao-I & Chen, Chi-Chung, 2007. "The relationship between GDP and electricity consumption in 10 Asian countries," Energy Policy, Elsevier, vol. 35(4), pages 2611-2621, April.
    6. Zeileis, Achim, 2004. "Econometric Computing with HC and HAC Covariance Matrix Estimators," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i10).
    7. Backus, David K & Kehoe, Patrick J, 1992. "International Evidence of the Historical Properties of Business Cycles," American Economic Review, American Economic Association, vol. 82(4), pages 864-888, September.
    8. J. Vernon Henderson & Adam Storeygard & David N. Weil, 2012. "Measuring Economic Growth from Outer Space," American Economic Review, American Economic Association, vol. 102(2), pages 994-1028, April.
    9. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    10. Jaqueson K. Galimberti, 2020. "Forecasting GDP Growth from Outer Space," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(4), pages 697-722, August.
    11. Kees Jan Van Garderen & Chandra Shah, 2002. "Exact interpretation of dummy variables in semilogarithmic equations," Econometrics Journal, Royal Economic Society, vol. 5(1), pages 149-159, June.
    12. Giuseppe Cavaliere & Luca Fanelli & Attilio Gardini, 2008. "International dynamic risk sharing," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(1), pages 1-16.
    13. Halvorsen, Robert & Palmquist, Raymond, 1980. "The Interpretation of Dummy Variables in Semilogarithmic Equations," American Economic Review, American Economic Association, vol. 70(3), pages 474-475, June.
    14. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    15. Taylor, James W. & de Menezes, Lilian M. & McSharry, Patrick E., 2006. "A comparison of univariate methods for forecasting electricity demand up to a day ahead," International Journal of Forecasting, Elsevier, vol. 22(1), pages 1-16.
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    Cited by:

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    3. Lazo, Joaquín & Aguirre, Gerson & Watts, David, 2022. "An impact study of COVID-19 on the electricity sector: A comprehensive literature review and Ibero-American survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
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    5. Jan Niklas Buescher & Daria Gottwald & Florian Momm & Alexander Zureck, 2022. "Impact of the COVID-19 Pandemic Crisis on the Efficiency of European Intraday Electricity Markets," Energies, MDPI, vol. 15(10), pages 1-21, May.
    6. Chen Zhu & Rigoberto A. Lopez & Yuan Gao & Xiaoou Liu, 2021. "The COVID‐19 Pandemic and Consumption of Food away from Home: Evidence from High‐frequency Restaurant Transaction Data," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 29(6), pages 73-94, November.
    7. Marcin Malec & Grzegorz Kinelski & Marzena Czarnecka, 2021. "The Impact of COVID-19 on Electricity Demand Profiles: A Case Study of Selected Business Clients in Poland," Energies, MDPI, vol. 14(17), pages 1-17, August.
    8. Abel Brodeur & David Gray & Anik Islam & Suraiya Bhuiyan, 2021. "A literature review of the economics of COVID‐19," Journal of Economic Surveys, Wiley Blackwell, vol. 35(4), pages 1007-1044, September.
    9. García, Sebastián & Parejo, Antonio & Personal, Enrique & Ignacio Guerrero, Juan & Biscarri, Félix & León, Carlos, 2021. "A retrospective analysis of the impact of the COVID-19 restrictions on energy consumption at a disaggregated level," Applied Energy, Elsevier, vol. 287(C).
    10. Eryarsoy, Enes & Shahmanzari, Masoud & Tanrisever, Fehmi, 2023. "Models for government intervention during a pandemic," European Journal of Operational Research, Elsevier, vol. 304(1), pages 69-83.
    11. Jacek Artur Strojny & Michał Stanisław Chwastek & Elżbieta Badach & Sławomir Jacek Lisek & Piotr Kacorzyk, 2022. "Impacts of COVID-19 on Energy Expenditures of Local Self-Government Units in Poland," Energies, MDPI, vol. 15(4), pages 1-25, February.
    12. Cerqueira, Pedro André & Pereira da Silva, Patrícia, 2023. "Assessment of the impact of COVID-19 lockdown measures on electricity consumption – Evidence from Portugal and Spain," Energy, Elsevier, vol. 282(C).
    13. Jasper Verschuur & Elco E Koks & Jim W Hall, 2021. "Global economic impacts of COVID-19 lockdown measures stand out in high-frequency shipping data," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-16, April.
    14. Luis M. Abadie, 2021. "Energy Market Prices in Times of COVID-19: The Case of Electricity and Natural Gas in Spain," Energies, MDPI, vol. 14(6), pages 1-17, March.
    15. Tung Le Thanh, 2022. "Relationship between the COVID-19 pandemic and the macroeconomic indicators: Evidence from a global analysis," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 16(1), pages 785-791, August.
    16. Ai, Hongshan & Zhong, Tenglong & Zhou, Zhengqing, 2022. "The real economic costs of COVID-19: Insights from electricity consumption data in Hunan Province, China," Energy Economics, Elsevier, vol. 105(C).
    17. Gagnon, Joseph E. & Kamin, Steven B. & Kearns, John, 2023. "The impact of the COVID-19 pandemic on global GDP growth," Journal of the Japanese and International Economies, Elsevier, vol. 68(C).
    18. Barbara Kowal & Robert Ranosz & Łukasz Herezy & Wojciech Cichy & Olga Świniarska & Lucia Domaracka, 2022. "Overview of Taken Initiatives and Adaptation Measures in Polish Mining Companies during a Pandemic," Energies, MDPI, vol. 15(17), pages 1-20, September.
    19. Werth, Annette & Gravino, Pietro & Prevedello, Giulio, 2021. "Impact analysis of COVID-19 responses on energy grid dynamics in Europe," Applied Energy, Elsevier, vol. 281(C).
    20. Fezzi, Carlo & Fanghella, Valeria, 2021. "Tracking GDP in real-time using electricity market data: Insights from the first wave of COVID-19 across Europe," European Economic Review, Elsevier, vol. 139(C).
    21. Enza Simeone, 2024. "Assessing the effect of the COVID-19 pandemic on wellbeing: a comparison between CBA and SWF approaches for policies evaluation," Working Papers 662, ECINEQ, Society for the Study of Economic Inequality.

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