Machine Learning Methods for Multi-Horizon Inflation Forecasting: A Comparative Analysis for Costa Rica

Resumen

This paper evaluates the performance of machine learning (ML) methods for forecasting year-over-year inflation in Costa Rica using monthly data from 2012-2025 and compares their performance against standard benchmarks within a rolling out-of-sample framework. ML techniques are particularly useful for capturing nonlinearities and complex interactions between inflation and a broad set of macroeconomic covariates. The results show that nonlinear ensemble methods such as XGBoost and BART provide the strongest gains at short horizons, while linear shrinkage methods are more competitive at longer horizons.