Inflation prediction in emerging economies: Machine learning and FX reserves integration for enhanced forecasting

Abstract

The present study makes two significant contributions to the extended body of literature in the context of International Finance. First, it forecasts the inflation in an emerging economy by employing a combination of traditional forecasting and Machine Learning models to test whether machine learning models outperform traditional forecasting models. Second, it explicitly includes an often-neglected variable i.e. foreign exchange reserves into the forecasting models to ascertain whether its inclusion enhances predictive accuracy. The outcomes of the study revealed interesting findings. It is observed that machine learning models consistently outperform traditional models, with Random Forest and Gradient Boosting are the top performers across different sets of determinants. Moreover, the study unveils that the inclusion of foreign exchange reserves into the models as a determinant has a positive impact on the predictive effectiveness of both traditional and machine learning-based inflation forecasting models.