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

IF 3.4 3区 经济学 Q1 ECONOMICS Journal of Forecasting Pub Date : 2024-03-05 DOI:10.1002/for.3116
Robert Lehmann, Sascha Möhrle
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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.

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利用新型高频用电数据预测地区工业生产
本文研究了电力消费数据对地区经济活动的预测能力。利用德国第二大州巴伐利亚自由州工业企业的独特高频用电数据,我们对巴伐利亚工业生产的月增长率进行了一次伪样本外预测实验。我们发现,在月度预测实验中,用电量是现在预测设置中表现最好的指标,其准确性高于其他传统指标。利用数据的高频特性,我们发现每周用电量指标也能很好地预测当前月份的工业活动,而信息量只有两周。总之,我们的研究结果表明,地区用电量是衡量和预测地区经济活动的一个很有前景的途径。
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来源期刊
CiteScore
5.40
自引率
5.90%
发文量
91
期刊介绍: The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.
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