Assessing the Sectorial Contributions to Gross Domestic Product (GDP) Using Principal Component Regression Approach (1981–2024)

Abstract

This study assessed the sectorial contributions to Nigeria’s Gross Domestic Product (GDP) using the Principal Component Regression (PCR) approach over the period 1981–2024. An ex post facto research design was adopted, using annual time-series data obtained from the 2024 Central Bank of Nigeria (CBN) Statistical Bulletin. Multiple Linear Regression (MLR) and PCR were employed to evaluate the contributions of 22 economic sectors while addressing multicollinearity. The results showed that the conventional MLR model was severely affected by multicollinearity, leading to unstable parameter estimates. PCR, however, reduced the sectorial variables to three orthogonal principal components that explained 98.91% of the total variation and produced a highly robust model with an adjusted R² of 99.99%. The principal components exerted significant positive effects on GDP, while exchange rate had a significant negative effect and trade openness a significant positive effect. Diagnostic tests confirmed that the final model was free from serial correlation and heteroskedasticity, indicating reliable estimates. Back-transformation revealed that Water Supply, Sewage and Waste Management; Construction; Electricity, Gas, Steam and Air Conditioning; Manufacturing; and Information and Communication were among the strongest contributors to GDP, while Mining and Quarrying and Public Administration contributed relatively less. The study concludes that PCR provides a reliable framework for assessing sectorial contributions to economic growth and recommends increased investment in infrastructure, utilities, manufacturing, and information and communication to promote sustainable growth and structural transformation in Nigeria.