Forecasting Regional Industrial Production with High-Frequency Electricity Consumption Data

Arbeitspapier

In this paper, we study the predictive power of electricity consumption data for regional economic activity. Using unique weekly and monthly electricity consumption data for the second-largest German state, the Free State of Bavaria, we conduct a pseudo out-of-sample forecasting experiment for the monthly growth rate of Bavarian industrial production. We find that electricity consumption is the best performing indicator in the nowcasting setup and has higher accuracy than other conventional indicators in a monthly forecasting experiment. Exploiting the high-frequency nature of the data, we find that the weekly electricity consumption indicator also provides good predictions about industrial activity in the current month even with only one week of information. Overall, our results indicate that regional electricity consumption offers a promising avenue to measure and forecast regional economic activity.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: CESifo Working Paper ; No. 9917

Klassifikation und Themen

• Klassifikation: Wirtschaft ; General Aggregative Models: Forecasting and Simulation: Models and Applications ; Macroeconomics: Consumption, Saving, Production, Employment, and Investment: Forecasting and Simulation: Models and Applications ; Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
• Thema: electricity consumption ; real-time indicators ; forecasting ; nowcasting

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Lehmann, Robert ; Möhrle, Sascha
• Ereignis: Veröffentlichung
• (wer): Center for Economic Studies and ifo Institute (CESifo)
• (wo): Munich
• (wann): 2022

Weitere Informationen

• Handle: http://hdl.handle.net/10419/265952
• Letzte Aktualisierung: 10.03.2025, 11:42 MEZ