Inflation Dynamics in the U.S. Economy: A VAR-GARCH Analysis of Oil Prices, Money Supply, and Employment

Understanding the complex dynamics of inflation in the United States economy remains a critical challenge for policymakers, investors, and macroeconomic theorists. This study investigates the multifaceted drivers of U.S. inflation by employing an advanced Vector Autoregression (VAR) combined with a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) framework. Specifically, the research examines the dynamic interplay and volatility spillovers among global oil prices, domestic money supply, employment levels, and the inflation rate over a comprehensive sample period. By integrating the VAR model to capture linear interdependencies and the GARCH model to account for time-varying volatility and risk, this paper provides a robust empirical analysis of macroeconomic shocks. The findings reveal significant Granger causality running from oil price fluctuations and money supply expansions to inflationary pressures, highlighting the vulnerability of the U.S. economy to both external energy shocks and internal monetary policy shifts. Furthermore, the employment data demonstrates a nuanced, lagged relationship with inflation, consistent with traditional Phillips curve expectations but complicated by recent structural economic changes. The volatility analysis underscores that uncertainty in oil markets disproportionately amplifies inflation variance compared to other macroeconomic indicators. Ultimately, this research contributes to the existing literature by offering a dual-layered perspective on both the directional impact and the volatility transmission mechanisms of key economic variables. The insights derived from this VAR-GARCH approach offer valuable implications for the Federal Reserve and other regulatory bodies in formulating more resilient monetary policies and stabilizing the macroeconomic environment against unforeseen exogenous and endogenous shocks.