Modeling and Forecasting Inflation Dynamics in Tanzania: Evidence from a Univariate ARIMA Approach

Abstract

Inflation, the sustained rise in prices for goods and services, carries profound implications for economic policies, financial markets, and individual well-being. Accurate forecasting of inflation is essential for economic stability. This paper uses univariate time series analysis to model and forecast Tanzania's inflation rate. The study employed the ARIMA model, utilizing the Box-Jenkins approach. Annual inflation rate data from the Tanzania National Bureau of Statistics were examined for trends, seasonality, and stationarity. Different techniques and stationarity tests are applied, and the selected ARIMA model undergoes rigorous diagnostics. Findings reveal that Tanzania's inflation has experienced fluctuations over the years, with periods of high inflation in the mid-1970s and early 1980s. Recent rates have remained relatively low and stable. The ARIMA (0, 1, 0) model, indicating the difference for stationarity, is chosen. The implications are significant, highlighting macroeconomic stability and favorable conditions for economic planning. However, policymakers must remain cautious of meagre inflation. In conclusion, the study forecasts a sustained environment of low and stable inflation in Tanzania from 2022 to 2030, reflecting effective monetary policies. This study contributes valuable insights for informed decision-making among businesses, consumers, and investors. Recommendations include monitoring economic indicators, maintaining flexible policies, promoting financial literacy, encouraging investment, and addressing regional economic challenges. This study enhances understanding of Tanzania's inflation dynamics and aids policymakers and stakeholders in navigating its economic landscape.