Modelling and prediction of economic growth for Nigeria under the violation of linear model assumptions: A robust principal component regression approach - Ayooluwade, EBIWONJUMI; Alamu, OBALADE Adefemi (Edelweiss Applied Science and Technology, 2025)
This study was conducted to model, estimate, and predict Nigeria’s economic growth (RGDP) by examining the influence of key macroeconomic drivers: internal debt (INDT), external debt (EXDT), interest rate (RINR), exchange rate (REXR), and the degree of economic openness (OPEN). Preliminary exploratory and diagnostic analyses revealed significant challenges to classical linear regression assumptions, particularly the presence of multicollinearity and outliers. To address these issues, robust principal component regression (PCR) estimation methods were employed. Principal component analysis (PCA) extracted two uncorrelated predictors (PC1 and PC2), which captured the joint variability of the original determinants while addressing collinearity. Subsequently, robust estimation techniques—namely M-estimation, S-estimation, and MM-estimation—were used to generate efficient estimated parameters. A comparative evaluation based on root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and Theil’s inequality coefficient established that the M-estimation method outperformed its alternatives, providing the most stable and reliable predictions of RGDP. Empirical findings revealed that both PC1 and PC2 had positive and statistically significant influences on RGDP, with contributions of 35.39% and 22.15%, respectively. These results highlight the importance of robust PCR in addressing econometric anomalies and offer valuable policy insights into how structural shocks—such as exchange rate volatility, oil price fluctuations, and COVID-19 disruptions—affected Nigeria’s economic performance.