Dynamic Time Warping (DTW) offers a superior approach to measuring economic cycle synchronization compared to traditional correlation methods. DTW handles phase shifts between time series by allowing optimal alignment through stretching and compression, making it ideal for comparing economic cycles that share similar patterns but are offset in time. The technique constructs composite business and financial cycle indices for Eurozone countries and computes pairwise DTW distances to create a real-time divergence monitor. Key advantages include phase-invariance, shape sensitivity, and time-varying flexibility, enabling better detection of economic synchronization patterns that traditional metrics might miss.
Table of contents
Capturing phase shifts and amplitude differencesDynamic Time Warping (DTW) for cycle synchronizationBuilding composite business and financial cyclesFrom pairwise DTW distances to an aggregate divergence monitorTakeaways for data scientists and economistsSort: