Foreign exchange rates, oils price, domestic economic policies, supply chain disruption and inflation rate in Nigeria: Evidence from ensemble learning algorithms

Abstract

This study investigates the drivers of inflation in Nigeria, focusing on the influence of foreign currencies, oil prices, domestic economic policies and supply chain disruption. With Nigeria experiencing an unprecedented inflation rate, the study employs five ensemble learning algorithms; Decision Tree, Random Forest, AdaBoost, Bagging, and Gradient Boosting regressions to uncover the relationships between inflation rates and its potential drivers. These drivers considered are exchange rates of foreign currencies, oil prices, domestic economic policies (the removal of oil subsidies and the floating of the Naira), and supply chain disruptions linked to the ongoing Russian-Ukrainian War. To better capture the effect of oil subsidy removal, floating of the Naira and Russia–Ukraine war, the continuous time-based variables which created a time counters that track the number of periods since these events occurred was used. The Exchange Rate Pass-Through (ERPT) Theory and Cost-Push Inflation Theory provided the theoretical framework for this study. Data from the Central Bank of Nigeria, covering January 2012 to September 2024, were used and the time series cross validation was used to ensure robustness against temporal dependencies while multicollinearity analysis was carried out using the Variance Inflation Factor (VIF) followed by PCA for dimensionality reduction. To estimate the possible effect of outliers the result was presented with and without robust scaler. Preprocessing was carried out, hyperparameters tuning were performed using Grid Search algorithm, and model performance was evaluated using metrics such as Mean Absolute Error, R-squared, Root Mean Square Error, Huber loss and adjusted R-squared using five-fold cross-validation. The results reveal a significant positive relationship between foreign currency exchange rates and inflation \( p < .05 \) with evidence of multicollinearity among features and hence the Principal Component Analysis (PCA) was used in dimensionality reduction. The use of robust scaler was found to improve the performance of the Machine Learning algorithms and the Gradient Boost algorithms outperformed other machine learning algorithms with the least RMSE (1.2990), MAE (0.8132) and Huber loss (0.5036) and the highest values of \( R^2 \) (0.9671) and adjusted \( R^2 \)(0.9634). Additional analysis using feature importance, Shapley Additive exPlanations (SHAP) and Partial Dependence (PP) plots showed that domestic policies variables particularly the removal of fuel subsidy and the floating of the Naira policy had the most significant positive impact on inflation in Nigeria while exchange rate of CFA and other global currencies such as USD and Euro were found to have moderate impact on inflation. These findings underscore an urgent need to reinvest savings from the subsidy removal into developmental projects such as building of refineries while also providing incentives and financial support to cushion the effect of subsidy removal. The need to establish currency swap agreements with key trade partners particularly China will help stabilize the Naira and its exposure to foreign currency volatility. These strategies could help in improving external balances, support industrial growth, and promote long –term resilience of Nigerian economy.