An Adaptive Evolutionary Causal Dynamic Factor Model

Background: With COVID-19 having a significant impact on economic activity, it has become difficult for the existing dynamic factor models (nowcasting models) to forecast macroeconomics with high accuracy. The real-time monitoring of macroeconomics has become an important research problem faced by banks, governments, and corporations. Subjects and Methods: This paper proposes an adaptive evolutionary causal dynamic factor model (AcNowcasting) for macroeconomic forecasting. Unlike the classical nowcasting models, the AcNowcasting algorithm has the ability to perform feature selection. The criteria for feature selection are based on causality strength rather than being based on the quality of the prediction results. In addition, the factors in the AcNowcasting algorithm have the capacity for adaptive differential evolution, which can generate the best factors. These two abilities are not possessed by classical nowcasting models. Results: The experimental results show that the AcNowcasting algorithm can extract common factors that reflect macroeconomic fluctuations better, and the prediction accuracy of the AcNowcasting algorithm is more accurate than that of traditional nowcasting models. Contributions: The AcNowcasting algorithm provides a new prediction theory and a means for the real-time monitoring of macroeconomics, which has good theoretical and practical value.