Unveiling the Power of Wavelet Power Spectrum (WPS) Analysis: A Deep Dive into Time Series Plots and Frequency Variations

In a recent study published in Nature, researchers conducted a detailed analysis of wavelet power spectrum (WPS) plots for various variables including fisheries, global land and ocean temperatures, and per capita GDP. The study aimed to uncover long-run cyclical patterns and short-run frequencies in these time series data.

The WPS plots revealed dominant frequency bands between 64 and 128 years cycles, indicating long-run cyclical patterns in the series. The analysis also captured bouts of short-run frequencies for Fisheries, global temperatures, and GDP, highlighting specific periods of significant variation.

The researchers then conducted wavelet coherence analysis to examine the co-movement between different variables. The results showed positive relationships between per capita GDP and Fisheries catches, indicating a developmental growth path in South Africa. Additionally, the analysis revealed nonlinear phase dynamics between temperatures and Fisheries catches, suggesting a U-shaped relationship over the long- and medium-run.

Furthermore, the study included partial wavelet coherence analysis to control for the effects of temperatures on the relationship between GDP and Fisheries, as well as the relationship between temperatures and Fisheries. The results showed weaker coherence between GDP and Fisheries when controlling for temperatures, while the phase dynamics between temperatures and Fisheries remained consistent.

Overall, the study provides valuable insights into the complex relationships between economic development, environmental factors, and fisheries catches in South Africa. The findings contribute to a better understanding of the dynamics shaping these variables and can inform policy decisions related to sustainable development and resource management.