IDEAS home Printed from https://ideas.repec.org/p/trn/utwprg/2020-8.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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

Suggested Citation

  • Carlo Fezzi & Valeria Fanghella, 2020. "Real-time estimation of the short-run impact of COVID-19 on economic activity using electricity market data," DEM Working Papers 2020/8, Department of Economics and Management.
  • Handle: RePEc:trn:utwprg:2020/8
    as

    Download full text from publisher

    File URL: https://www.economia.unitn.it/alfresco/download/workspace/SpacesStore/f27b3176-e78c-4c9a-8978-aeead2633d7a/DEM2020_08.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Zeileis, Achim, 2004. "Econometric Computing with HC and HAC Covariance Matrix Estimators," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i10).
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Carlo Fezzi & Valeria Fanghella, 2020. "Real-Time Estimation of the Short-Run Impact of COVID-19 on Economic Activity Using Electricity Market Data," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 76(4), pages 885-900, August.
    3. 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).
    4. 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.
    5. Bashiri Behmiri, Niaz & Fezzi, Carlo & Ravazzolo, Francesco, 2023. "Incorporating air temperature into mid-term electricity load forecasting models using time-series regressions and neural networks," Energy, Elsevier, vol. 278(C).
    6. Mehdi Abid & Rafaa Mraihi, 2015. "Energy Consumption and Industrial Production: Evidence from Tunisia at Both Aggregated and Disaggregated Levels," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 6(4), pages 1123-1137, December.
    7. Beyer, Robert C.M. & Franco-Bedoya, Sebastian & Galdo, Virgilio, 2021. "Examining the economic impact of COVID-19 in India through daily electricity consumption and nighttime light intensity," World Development, Elsevier, vol. 140(C).
    8. Yang-Ho Park, 2019. "Information in Yield Spread Trades," Finance and Economics Discussion Series 2019-025, Board of Governors of the Federal Reserve System (U.S.).
    9. Menezes, Flavio & Figer, Vivian & Jardim, Fernanda & Medeiros, Pedro, 2022. "A near real-time economic activity tracker for the Brazilian economy during the COVID-19 pandemic," Economic Modelling, Elsevier, vol. 112(C).
    10. Kajal Lahiri & Liu Yang, 2018. "Confidence Bands for ROC Curves With Serially Dependent Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 115-130, January.
    11. Evangelista, Rui & Ramalho, Esmeralda A. & Andrade e Silva, João, 2020. "On the use of hedonic regression models to measure the effect of energy efficiency on residential property transaction prices: Evidence for Portugal and selected data issues," Energy Economics, Elsevier, vol. 86(C).
    12. Timo Dimitriadis & iaochun Liu & Julie Schnaitmann, 2023. "Encompassing Tests for Value at Risk and Expected Shortfall Multistep Forecasts Based on Inference on the Boundary," Journal of Financial Econometrics, Oxford University Press, vol. 21(2), pages 412-444.
    13. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    14. Paul Ghelasi & Florian Ziel, 2024. "From day-ahead to mid and long-term horizons with econometric electricity price forecasting models," Papers 2406.00326, arXiv.org, revised Aug 2024.
    15. Omoniyi Alimi & Geua Boe-Gibson & John Gibson, 2022. "Noisy Night Lights Data: Effects on Research Findings for Developing Countries," Working Papers in Economics 22/12, University of Waikato.
    16. Raviv, Eran & Bouwman, Kees E. & van Dijk, Dick, 2015. "Forecasting day-ahead electricity prices: Utilizing hourly prices," Energy Economics, Elsevier, vol. 50(C), pages 227-239.
    17. Silvers, Roger, 2021. "Does regulatory cooperation help integrate equity markets?," Journal of Financial Economics, Elsevier, vol. 142(3), pages 1275-1300.
    18. Gary Gorton & Frank Schmid, 2000. "Class Struggle Inside the Firm: A Study of German Codetermination," NBER Working Papers 7945, National Bureau of Economic Research, Inc.
    19. Adermon, Adrian & Liang, Che-Yuan, 2014. "Piracy and music sales: The effects of an anti-piracy law," Journal of Economic Behavior & Organization, Elsevier, vol. 105(C), pages 90-106.
    20. Christopher Kath, 2019. "Modeling Intraday Markets under the New Advances of the Cross-Border Intraday Project (XBID): Evidence from the German Intraday Market," Energies, MDPI, vol. 12(22), pages 1-35, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:trn:utwprg:2020/8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: roberto.gabriele@unitn.it (email available below). General contact details of provider: https://edirc.repec.org/data/detreit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.