The Methodological Evolution of Macroeconomic Early Warning Systems: From Econometric Models to Real-Time Data Analytics

Abstract

Macroeconomic Early Warning Systems (MEWS) have undergone significant methodological evolution, driven by advances in econometrics, data availability, and computational power. This review paper traces this evolution, beginning with traditional econometric models rooted in linear regressions and time series analysis. We explore the shift towards more sophisticated non-linear models, including threshold models, Markov-switching models, and machine learning techniques. A central theme is the increasing use of real-time data and high-frequency indicators to improve the timeliness and accuracy of early warning signals. We examine the challenges associated with data quality, model validation, and the interpretation of results in a policy context. The paper further delves into the integration of diverse data sources, such as financial market data, sentiment analysis, and global value chain information, to enhance the robustness of MEWS. Finally, we discuss future directions, including the development of explainable AI (XAI) methods for MEWS and the application of causal inference techniques to identify the underlying drivers of macroeconomic instability. This review provides a comprehensive overview of the methodological landscape of MEWS, highlighting both the progress made and the challenges that remain.