Forecasting Inflation Patterns in Tanzania: A Time Series Modeling.

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 employs the ARIMA model to forecast the inflation patterns. Annual inflation rate data from the Tanzania National Bureau of Statistics were examined for trends, seasonality, and stationarity. Stationarity tests were applied, and the selected ARIMA model undergoes rigorous diagnostics. The Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) were used to identify potential orders for the ARIMA model (p, d, q) and the ARIMA (0, 1, 0) model was chosen as a best model after the analysis. The study forecasts a sustained environment of low and stable inflation in Tanzania from 2022 to 2030, reflecting effective monetary policies. The forecasted values highlight macroeconomic stability and favorable conditions for economic planning. The study therefore, enhances understanding of Tanzania's inflation dynamics and aids policymakers and stakeholders in navigating its economic landscape.