<b>Evaluation of Policy-Relevant Forecasting Advantage of Machine and Deep Learning Architectures over Classical Econometrics: Evidence from Ghanaian Headline Inflation</b>

Gabriel Osei Forkuo, Baffour Osei, Isaac Nyame, Abigail Boatemaa

This study evaluates whether machine learning and deep learning models offer a genuine forecasting advantage over classical econometric benchmarks for Ghana’s headline (year-on-year) inflation, a question of direct relevance to a central bank operating an inflation-targeting regime in a small, commodity-exposed, currency-volatile economy. Using 280 months (January 2000–April 2023) of Bank of Ghana monetary and macro-financial data, we construct a strictly one-step-ahead forecasting matrix in which every predictor reflects information available one month before the forecast target, and compare nine models (a random walk and seasonal naive baseline, an AIC-selected ARIMA benchmark, two regularized linear models [Ridge, Lasso], two tree ensembles [Random Forest, XGBoost], a kernel method [Support Vector Regression], and a shallow feed-forward neural network) on a chronological train/validation/test split whose 42-month test window (October 2019–April 2023) deliberately spans Ghana’s 2022–23 inflation surge.