Author: admin

Summary

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.