Regime Switching Models in Finance and Economics: Traditional Approaches And Machine Learning Enhancements

Regime-switching models are a vital class of econometric and statistical tools that allow parameters or data-generating processes to change across different periods or “regimes.” This paper surveys traditional regime-switching approaches, including Markov-switching, threshold, and hidden Markov models, reviewing their applications in finance, investments, and economics. We summarize how these models capture phenomena like business cycles, market phases (bull/bear), volatility regimes, and interest rate dynamics. We highlight successful identification of regimes such as recessions versus expansions and distinct market return states. Furthermore, we explore the integration of machine learning (ML), particularly using feature importance from ensemble methods (e.g., random forests), to enhance regime identification and variable selection. We discuss the potential of these hybrid methods, citing recent research and case studies like early warning systems. The paper concludes with a comparative discussion of traditional versus ML-augmented approaches and outlines future directions for regime-switching analysis in the context of big data and complex financial systems.