Forecasting Disaggregated Food Inflation Baskets in Colombia with an XGBoost Model

Food prices have consistently been one of the leading contributors to Colombia’s inflation rate. They are particularly sensitive to exogenous factors such as extreme weather events, supply chain disruptions, and global commodity price shocks, often resulting in sharp and unpredictable price fluctuations. This document pursues two main objectives. First, it aims to estimate and evaluate methods for forecasting 33 homogeneous food inflation baskets, which together constitute the total food Consumer Price Index (Food CPI), offering tools that can assist policymakers in anticipating the drivers of future inflation. This includes both traditional time series models and modern machine learning approaches. Second, it seeks to enhance the interpretability of model predictions through explainable AI techniques. To achieve this, we propose a variable lag selection algorithm to identify optimal feature-lag pairs, and employ SHAP (SHapley Additive exPlanations) values to quantify the contribution of each feature to the model’s forecast. Our findings indicate that machine learning models outperform traditional approaches in forecasting food inflation, delivering improved accuracy across most individual baskets as well as for aggregated food inflation.