Composite GDP nowcasting using macroeconomic variables and electricity data - Zhiqiang Lan; Zidi Liu; Guoyao Wu (PLoS ONE, 2025)
Accurate and timely forecasting of the gross domestic product (GDP), known as “nowcasting”, is crucial for macroeconomic regulation. In this paper, we propose a composite GDP nowcasting model that combines predictions from a dynamic factor model using mixed-frequency macroeconomic indicators and a regression model using real-time electricity data. In the regression model, we introduce changes in electricity capacity, in addition to electricity consumption, as a new predictor to reflect expectations for future electricity demand. Such a nowcasting model not only leverages the correlation between GDP and other macroeconomic indicators, but also utilizes the information contained in electricity data, which is closely related to production. We perform nowcasting for year-on-year quarterly GDP growth rates using data from Fujian Province, China. The results demonstrate that the proposed composite nowcasting model effectively reduces forecast errors compared to the dynamic factor model and the regression model.