Abstract
In recent years, China’s macroeconomic landscape has been characterized by a confluence of challenges, including intensified global economic headwinds and the complexities associated with domestic structural economic transformation. Consequently, the macroeconomic environment has become increasingly intricate, with heightened sensitivity and rapid responsiveness to external shocks, posing formidable tests for China’s macroeconomic early warning capabilities. Traditional macroeconomic forecasting methodologies, predominantly reliant on monthly and quarterly economic and industrial statistics, exhibit inherent limitations due to their low frequency and sluggish data updates, rendering them inadequate for accurately predicting short-term macroeconomic fluctuations. Electricity consumption, being a pivotal component of terminal energy utilization across most industries, holds robust representativeness for the broader macroeconomic landscape. Leveraging the salient characteristics of electricity consumption data, this study amalgamates it with other high-frequency data to forecast industrial added value through the employment of the MIDAS (Mixed Data Sampling) model. The findings indicate that the integration of daily electricity consumption data into the MIDAS model can significantly enhance short-term prediction accuracy.
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