Inferring Economic Condition Uncertainty from Electricity Big Data

TL;DR

This paper introduces a novel method using Hidden Markov Models to derive a standardized Economic Condition Uncertainty index from electricity consumption data, effectively capturing economic impacts and heterogeneities during COVID-19 in Shanghai.

Contribution

It presents a new approach to measure economic uncertainty through electricity data and constructs a comparable index applicable across sectors and regions.

Findings

ECU indexes captured COVID-19's negative economic impacts

Heterogeneities in economic uncertainty across districts and sectors

ECU index can be extended to other economic realms

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.