Nowcasting Economic Growth with Machine Learning and Satellite Data

Eurydice Fotopoulou, Iyke Maduako, M. Belen Sbrancia and Prachi Srivastava

No 2026/020, IMF Working Papers from International Monetary Fund

Abstract: The absence of reliable data on fundamental economic indicators (e.g. real GDP), combined with structural shifts in the economy, can severely constrain the ability to conduct accurate macroeconomic analysis and forecasting. This paper explores alternatives to address data limitations by integrating machine learning and satellite data to estimate real GDP. Specifically, it finds that incorporating satellite-based nightlight data into a random forest model significantly improves the accuracy of quarterly GDP growth estimates compared with models relying solely on traditional indicators. This empirical application contributes to the emerging nowcasting field to enhance economic forecasting in economies with significant data gaps.

Keywords: Macroeconomic forecast; Machine learning; Nowcasting; GDP; Satellite data; Random Forest; data limitation; Eurydice Fotopoulou; data gap; IMF working paper; Oil; Sub-Saharan Africa; South America; Central America; Caribbean (search for similar items in EconPapers)
Pages: 35
Date: 2026-01-30
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