GDP Nowcasting in Mauritania using Machine Learning and Satellite Data

This paper examines the role of satellite data and machine learning techniques in improving the nowcasting of real GDP in Mauritania, a context characterized by limited data availability and delays in official statistics. By combining traditional macroeconomic indicators with satellite-based variables such as Nighttime Lights and the Normalized Difference Vegetation Index (NDVI), the study develops a framework capable of capturing real-time economic dynamics. The results show that the inclusion of satellite data improves predictive accuracy, particularly when using non-linear models such as XGBoost. In particular, XGBoost records a reduction in RMSE from 0.019 to 0.017 and an increase in R² from 0.372 to 0.483 when satellite variables are included. Nighttime Lights are strongly correlated with economic activity, while NDVI exhibits more limited explanatory power. Overall, the findings highlight the potential of integrating alternative data sources and machine learning methods to enhance economic monitoring and support decisionmaking in data-constrained environments.