This study applied Empirical Mode Decomposition (EMD) to analuse key economic indicators: Compensation, GDP, and Employment Rate within German economy from Q1 2005 to Q4 2023. EMD, a method suited for non-stationary and nonlinear data, is utilized to decompose these variables into Intrinsic Mode Functions (IMFs) and a residual component. The IMFs allow for the identification of underlying cyclical patterns and long-term trends in each indicator, providing a more detailed understanding of their behavior over time. This paper explores the inter-dependencies among these economic variables by performing correlation and Granger causality tests. Specifically, we aim to assess how changes in one variable influence the others and uncover potential predictive relationships. The findings contribute to a more nuanced understanding of economic fluctuations, offering insights valuable for policy formulation and economic decision-making. By addressing both short-term cycles and long-term trends, this analysis enhances the ability to interpret dynamic interactions in the German economy.