Box-Jenkins modelling of inflation rates in Ghana : a data-driven approach

Abstract

Inflation is a pivotal economic indicator that influences business activities and livelihoods. The escalating trend of inflation rates globally has led to business closures and widespread economic hardship. This study employs the Box-Jenkins methodology to develop predictive SARIMA and SARIMAX models for Ghana’s monthly inflation rates. The models capture both seasonal and non-seasonal inflation rate components, as well as the impact of external factors such as the Ghana-US exchange rates. Our findings indicate that the SARIMA model provides more accurate inflation rate predictions. The results have significant implications for monetary policy and inflation targeting in Ghana. Furthermore, the methodology is universally applicable to macroeconomic forecasting problems.