The time series analysis of Nigeria’s gross product (GDP) using the ARIMA model

Abstract

This study applies the Box–Jenkins Autoregressive Integrated Moving Average (ARIMA) methodology to Nigeria’s Gross Domestic Product (GDP) using annual data from 1960 to 2020. GDP, a central measure of economic performance, reflects both growth potential and structural fragility, particularly Nigeria’s dependence on crude oil revenues and exposure to external shocks. Unit root tests confirmed non-stationarity in the raw series, requiring first differencing to achieve stationarity. Several ARIMA specifications were evaluated using information criteria, and ARIMA (1,1,1) emerged as the optimal model, balancing simplicity and predictive accuracy. Parameter estimates revealed significant autoregressive and moving average components, while diagnostic checks validated the adequacy of the model, confirming that residuals approximate white noise. Forecasts captured persistence and short-term shocks, projecting plausible growth trajectories for Nigeria’s economy. The findings underscore Nigeria’s vulnerability to oil price volatility and global crises such as the COVID-19 pandemic. While ARIMA provides reliable short-term forecasts, its inability to anticipate rare “black swan” events highlights the need for complementary scenario analysis and policy judgment. The study demonstrates that simple ARIMA models can yield robust forecasts in developing economies, offering evidence-based insights for fiscal and monetary planning. Sustainable growth, however, requires diversification and resilience beyond statistical forecasting, ensuring that Nigeria’s economy can withstand external shocks and achieve long-term stability.