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Inferring Economic Condition Uncertainty from Electricity Big Data

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  • Haoqi Qian
  • Zhengyu Shi
  • Libo Wu

Abstract

Inferring the uncertainty in economic conditions is significant for both decision makers as well as market players. In this paper, we propose a novel approach to measure the economic uncertainties by using the Hidden Markov Model (HMM). We construct a dimensionless index, Economic Condition Uncertainty (ECU) index, which ranges from zero to one and is comparable among sectors, regions and periods. We used the daily electricity consumption data of more than 18,000 firms in Shanghai from 2018 to 2020 to construct the ECU indexes. Results show that all ECU indexes, whether at sectoral or regional level, successfully captured the negative impacts of COVID-19 on Shanghai's economic conditions. Besides, the ECU indexes also presented the heterogeneities in different districts as well as in different sectors. This reflects the facts that changes in the uncertainty of economic conditions are mainly related to regional economic structures and targeted regulatory policies faced by sectors. The ECU index can also be readily extended to measure the uncertainty of economic conditions in various realms, which has great potentials in the future.

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  • Haoqi Qian & Zhengyu Shi & Libo Wu, 2021. "Inferring Economic Condition Uncertainty from Electricity Big Data," Papers 2107.11593, arXiv.org, revised May 2023.
    • Handle: RePEc:arx:papers:2107.11593
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