Forecasting regional industrial production with novel high‐frequency electricity consumption data

Robert Lehmann and Sascha Möhrle

Journal of Forecasting, 2024, vol. 43, issue 6, 1918-1935

Abstract: In this paper, we study the predictive power of electricity consumption data for regional economic activity. Using unique high‐frequency electricity consumption data from industrial firms 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 with only 2 weeks of information. Overall, our results indicate that regional electricity consumption is a promising avenue for measuring and forecasting regional economic activity.

Date: 2024
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https://doi.org/10.1002/for.3116

Related works:
Working Paper: Forecasting Regional Industrial Production with High-Frequency Electricity Consumption Data (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:43:y:2024:i:6:p:1918-1935

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