Abstract:

This study investigates the impact of inflation on foreign travel dynamics in Nigeria, focusing on the Headline Inflation Rate (HIR), Core Inflation Rate (CIR), and Food Inflation Rate (FIR) as predictors of the number of passengers travelling abroad (PPF) and the percentage of aircraft travelling internationally (PAF). Secondary data was employed in this study. The Central Bank of Nigeria Statistical Bulletin 2021 and the Federal Airport Authority of Nigeria (FAAN) records from 2015–2020 were the sources of secondary data. Correlation analysis, the linear regression model, the random forest regression model, and the gradient boosting regression model are among the statistical tools used. Correlation analysis revealed significant relationships among variables, with a strong positive correlation between PPF and PAF (0.95384) and between CIR and FIR (0.75894). In contrast, HIR exhibited weak negative correlations with PPF (-0.3024) and PAF (-0.24953). Linear regression models indicated statistical significance (F-statistic = 4.102, p = 0.0106), with HIR and FIR negatively impacting PPF (p = 0.0048, p = 0.0146, respectively) and CIR positively influencing it (p = 0.0419). However, these models explained only 18% of the variability in outcomes (adjusted R² = 0.1362). Machine learning models, particularly Random Forest, demonstrated superior predictive performance, explaining 51.24% and 55.23% of the variance in PPF and PAF, respectively, with the lowest RMSE values. Gradient Boosting also outperformed linear regression. HIR was the most influential predictor for PPF, while FIR dominated for PAF. These findings highlight the nuanced effects of inflation on travel dynamics and underscore the advantages of machine learning in policy modelling. Future research should explore additional factors, such as exchange rates and consumer confidence, to enhance understanding.