The Effect of Exchange Rate Dynamics on Food Prices in Urban Uganda: An Integrated Machine Learning and Econometrics Analysis of Kampala Markets

Abstract

Food price volatility remains a major policy concern in urban Uganda because changes in staple food prices directly affect household welfare, food security, and the purchasing power of lowincome consumers. This study investigated the effect of UGX/USD exchange rate dynamics on food prices in Kampala markets using a multi-method analytical framework that combines econometric analysis, causal inference, non-linear modelling, frequency-domain analysis, machine learning, and model deployment. The study used World Food Programme food-price data for Uganda and official Bank of Uganda exchange-rate data, merged on a monthly basis to create an analytical panel covering January 2006 to September 2025. The analysis began with data preprocessing and exploratory data analysis to examine foodprice trends, commodity-level volatility, and consistency between implied and official exchange rates. Causal and dynamic relationships were then evaluated using regression analysis, Granger causality testing, transfer entropy, DCC-GARCH, Markov-switching regression, quantile regression, and wavelet coherence. Predictive performance was assessed using traditional regression models, Random Forest, XGBoost, Prophet, and deep learning models, with model accuracy evaluated using R2, RMSE, and MAE. Finally, selected models were operationalized through a Flask API and Progressive Web Application frontend to demonstrate practical food-price prediction. The findings show that exchange rate movements contain meaningful information for understanding and forecasting urban food prices, but the relationship is non-linear, commodityspecific, and time-varying. Causal evidence indicates a predominantly one-directional influence from exchange rates to food prices, while regime and quantile results show that pass-through effects intensify during high-volatility and high-price episodes. Wavelet analysis further shows strong short-term co-movement at the 3–6 month horizon, with weaker associations at longer horizons. In predictive modelling, XGBoost achieved the strongest panel-level performance with an R2 of 0.7944, RMSE of 646.25, and MAE of 401.60, followed closely by Random Forest with an R2 of 0.7815. These results demonstrate that machine learning models using exchangerate and commodity information can substantially improve food-price prediction compared with linear baselines. The study contributes theoretically by extending exchange rate pass-through analysis to urban food markets, methodologically by integrating causal, non-linear, time-frequency, and predictive approaches, and practically by deploying a web-based decision-support tool for commodity price prediction. The findings suggest that food-price stabilization policies in Uganda should combine exchange-rate monitoring with commodity-specific market intelligence and early warning systems.