Linearly penalized segmentation, binary segmentation, bottom-up segmentation and window-based methods for change point detection

This paper presents a comparative analysis of various algorithms for change point detection in economic data, focusing on their detection accuracy, and computational efficiency. Change point detection is crucial for identifying structural shifts in economic indicators, which can inform policy decisions and economic forecasting. We evaluate a selection of widely used algorithms including linearly penalized segmentation, binary segmentation, bottom-up segmentation, window-based change point detection using economic datasets encompassing different time periods and environments. By utilizing real market data, we demonstrate that while some algorithms excel in detecting abrupt changes, others are more effective in identifying gradual shifts in macroeconomic environment. The comparative study reveals that no single algorithm uniformly outperforms others across all, suggesting the need for a context-dependent application of these methods. The results underscore the importance of algorithm selection tailored to specific economic conditions and the nature of the data. This research provides a comprehensive guidance for economists and data scientists in choosing the efficient change point detection algorithm, thereby enhancing the robustness of economic analysis in the era of big data.