^{<strong>Evaluating Machine Learning and Econometric Models for Inflation Forecasting: Evidence from a Sub-Saharan African Panel</strong>}

This study constructs a reproducible, annual panel of macroeconomic indicators for eight Sub-Saharan African economies (1972–2024, N = 424) to compare eight forecasting approaches: a naive random-walk benchmark, two linear econometric models (pooled OLS and country fixed effects), three machine learning models (Elastic Net, Random Forest, and XGBoost), and a recurrent neural network (LSTM) evaluated with and without early stopping. Using a chronological train/validation/test split and pairwise Diebold-Mariano tests, we evaluate out-of-sample predictive accuracy.