Dynamic Modeling of Money Supply and GDP: A State-Space Approach

Abstract

Using data from 2008 to 2023, this study investigates the dynamic relationship between money supply (M2) and GDP through a state-space modeling approach. Addressing the question of how effectively money supply impacts GDP, the study is grounded in the theoretical framework that monetary policy influences economic output, a concept drawn from classical and Keynesian economic theories. The data, sourced from official economic reports, highlights significant variability in both M2 and GDP, with GDP exhibiting slightly lower volatility. The distributional characteristics of the variables show a slight positive skew and a flatter-than-normal distribution, suggesting potential deviations from typical economic trends. The state-space model captures the relationship between M2 and GDP, indicating that money supply is an important explanatory factor for GDP. While the model’s observation equation shows a low and statistically significant standard deviation of the error term—suggesting that it explains variations in GDP effectively—the state transition equation reveals a mostly deterministic evolution of unobserved state variables, though this result lacks full statistical significance and warrants cautious interpretation. The log-likelihood value supports a fair model fit, yet additional macroeconomic variables or alternate model structures may enhance the analysis. The findings affirm the critical role of money supply in economic growth, consistent with economic theory. However, future research could explore more complex dynamics, including non-linear relationships and the effects of external shocks, to refine the understanding of the M2-GDP nexus. This study contributes empirical insights using a state-space framework, offering valuable implications for monetary policy and macroeconomic forecasting.