International Journal For Multidisciplinary Research

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Forecasting USA’s Inflation Using ARCH and GARCH Models

Author(s) Joseph Adomako- Ansah, Diping Zhang, Mukendi Heritier Makasa
Country China
Abstract 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.
Keywords Inflation Forecasting, ARCH model, GARCH model, Economic Volatility, Time Series Analysis
Field Mathematics > Statistics
Published In Volume 7, Issue 2, March-April 2025
Published On 2025-03-14
DOI https://doi.org/10.36948/ijfmr.2025.v07i02.37288
Short DOI https://doi.org/g8949t
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