Forecasting Regional Industrial Production with High-Frequency Electricity Consumption Data

Robert Lehmann and Sascha Möhrle

No 9917, CESifo Working Paper Series from CESifo

Abstract: 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.

Keywords: electricity consumption; real-time indicators; forecasting; nowcasting (search for similar items in EconPapers)
JEL-codes: E17 E27 R11 (search for similar items in EconPapers)
Date: 2022
New Economics Papers: this item is included in nep-ene, nep-for, nep-geo and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Forecasting regional industrial production with novel high‐frequency electricity consumption data (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_9917

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