FORECASTING SOME MACROECONOMIC VARIABLES IN NIGERIA: EVIDENCE FROM ARIMA MODEL

Abstract

`This study aims to forecast main macroeconomic variables in Nigeria, including GDP, inflation rate, exchange rate, and unemployment rate, using the Autoregressive Integrated Moving Average (ARIMA) model. Employing monthly data from January 2010 to December 2023 sourced from the Central Bank of Nigeria, National Bureau of Statistics, International Monetary Fund, and the World Bank, the study rigorously applies the Box-Jenkins methodology. This involves data preprocessing, stationarity testing, model identification, parameter estimation, diagnostic checking, and forecasting. The Augmented Dickey-Fuller test confirmed the stationarity of the transformed series, and the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots guided model selection. The ARIMA model’s parameters were estimated using Maximum Likelihood Estimation, and the model’s adequacy was validated through residual diagnostic tests such as the Ljung-Box Q-test. The analysis demonstrated that ARIMA (1,1,1) effectively forecasted GDP, ARIMA (2,1,2) accurately predicted inflation, ARIMA (1,1,1) captured exchange rate dynamics, and ARIMA (2,1,1) forecasted unemployment by high predictive accuracy with low Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) values. The discussion highlights the robustness of the ARIMA model in capturing the underlying patterns and trends in Nigeria’s macroeconomic data. However, it also acknowledges the model’s limitations, such as its reliance on historical data and difficulty in accounting for sudden economic shocks. The study recommends integrating ARIMA models with other forecasting techniques, such as machine learning algorithms, to improve accuracy and robustness. In conclusion, this study stresses the ARIMA model’s utility in providing reliable short-term forecasts for vital macroeconomic variables in Nigeria. These forecasts can significantly assist policymakers in economic planning and decision-making. The findings emphasize the importance of high-quality data and advanced statistical techniques in enhancing forecast reliability and addressing the dynamic nature of Nigeria’s economy.