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Economic and Business Cycle Analyses with Electricity Consumption Data


  • Valeriya Azarova
  • Robert Lehmann
  • Sascha Möhrle
  • Andreas Peichl
  • Karen Pittel
  • Marie-Theres von Schickfus
  • Timo Wollmershäuser


The Corona crisis underscored the need for unconventional data sources to assess the current economic situation in a timely manner. The data situation is particularly difficult for the German states. This research report addresses the question whether high-frequency data on Bavarian electricity consumption are suitable for economic and business cycle analyses. First, it examines electricity consumption of large consumers with regard to energy efficiency, distribution of peak loads, and effects on air pollution. Second, we focus on the question whether electricity consumption is suitable for dating the Bavarian business cycle. Third, it is examined whether electricity consumption data can be used to forecast industrial production in Bavaria. Overall, the new data source proves to be promising and useful. In a purely descriptive analysis of energy efficiency, breaks are found in the time course of aggregated electricity consumption data, which could be attributed to the introduction of energy policy measures. For a causal interpretation, however, disaggregated data are required. In addition, differences in peak loads compared to Germany were found, as well as a positive influence of industrial electricity use on the main air pollutants. Furthermore, the aggregated electricity consumption data are suitable for dating the cyclical swings in Bavarian industrial production. Thus, especially the severe recessions as well as the subsequent recovery phases are reliably indicated by electricity consumption. In addition, the Bavarian electricity consumption data have a high predictive power for forecasting Bavaria's industrial production on both a monthly and weekly basis. When forecasting the current month (nowcast), Bavarian electricity consumption is the best indicator on average. However, when forecasting the next month, electricity consumption loses forecasting power.

Suggested Citation

  • Valeriya Azarova & Robert Lehmann & Sascha Möhrle & Andreas Peichl & Karen Pittel & Marie-Theres von Schickfus & Timo Wollmershäuser, 2022. "Economic and Business Cycle Analyses with Electricity Consumption Data," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 129.
  • Handle: RePEc:ces:ifofob:129

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    References listed on IDEAS

    1. Wohlrabe, Klaus, 2009. "Forecasting with mixed-frequency time series models," Munich Dissertations in Economics 9681, University of Munich, Department of Economics.
    2. Stefan Sauer & Klaus Wohlrabe, 2020. "ifo Handbuch der Konjunkturumfragen," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 88.
    3. Vosen, Simeon & Schmidt, Torsten, 2012. "A monthly consumption indicator for Germany based on Internet search query data," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 19(7), pages 683-687.
    4. Yue, Hui & Worrell, Ernst & Crijns-Graus, Wina, 2021. "Impacts of regional industrial electricity savings on the development of future coal capacity per electricity grid and related air pollution emissions – A case study for China," Applied Energy, Elsevier, vol. 282(PB).
    5. Beate Schirwitz, 2009. "A comprehensive German business cycle chronology," Empirical Economics, Springer, vol. 37(2), pages 287-301, October.
    6. Zhang, Shaohui & Worrell, Ernst & Crijns-Graus, Wina, 2015. "Evaluating co-benefits of energy efficiency and air pollution abatement in China’s cement industry," Applied Energy, Elsevier, vol. 147(C), pages 192-213.
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