Forecasting USA’s Inflation Using ARCH and GARCH Models

Forecasting inflation is crucial for effective economic policy and planning, particularly in the USA, where inflation dynamics impact global markets. This study explored the application of Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models in forecasting the USA’s inflation. The primary objective of this study was to assess the effectiveness of ARCH and GARCH models in forecasting inflation rates in the United States. This study utilized monthly data on the United States Consumer Price Index (CPI) as a proxy for inflation, spanning the period from January 2000 to December 2023. The CPI data is sourced from the U.S. Bureau of Labor Statistics (BLS), a reliable and widely used database for macroeconomic research. CPI represents the average change in prices paid by urban consumers for a basket of goods and services and is an essential indicator for assessing inflation trends. The positive skewness indicated a longer right tail, reflecting occasional high inflation spikes, such as those during the post-pandemic economic recovery. The kurtosis slightly exceeding 3 suggested that the distribution exhibits light tails compared to a normal distribution. In conclusion, ARCH and GARCH models offered robust tools for understanding inflation dynamics, with significant implications for economic stability and decision-making. Their adoption can empower policymakers and practitioners to navigate inflationary challenges with greater precision.