Modeling the Gross Domestic Product of Tanzania from 1960 to 2023

The Box- Jenkins Approach

Abstract

Gross Domestic Product (GDP) is a crucial indicator of a nation’s economic performance, reflecting overall economic activity and guiding policy formulation. Accurate GDP forecasting is essential for economic planning, especially in countries like Tanzania, where external shocks such as the COVID-19 pandemic had significantly influenced economic trends. Despite the importance of GDP forecasting, limited studies have analyzed the effectiveness of time series models in predicting Tanzania’s GDP before and after major economic shocks.

This study employed the Autoregressive Integrated Moving Average (ARIMA) model to forecast Tanzania’s GDP at current prices, comparing pre- and post-COVID-19 trends. The research utilizes historical GDP data from 1960 to 2023, obtained from the World Bank. The Box-Jenkins methodology is applied to identify and validate the best-fitting ARIMA model based on statistical criteria such as AIC, BIC, and RMSE. The findings indicate that the ARIMA (0,2,1) model effectively captured Tanzania’s GDP trends, offering reliable short-term forecasts. However, external factors such as inflation, global economic fluctuations, and structural inefficiencies continue to pose challenges to long-term economic stability.

The study highlights the significance of integrating time series forecasting into economic decision-making, enabling policymakers to anticipate economic shifts and implement evidence-based strategies.