Forecasting GDP growth from outer space - Jaqueson K. Galimberti (Auckland University of Technology (AUT), Faculty of Business, Economics and Law, 2020)
We evaluate the usefulness of satellite-based data on night-time lights for forecasting GDP growth across a global sample of countries, proposing innovative location-based indicators to extract new predictive information from the lights data. Our findings are generally favorable to the use of night lights data to improve the accuracy of model-based forecasts. We also find a substantial degree of heterogeneity across countries in the relationship between lights and economic activity: individually-estimated models tend to outperform panel specifications. Key factors underlying the night lights performance include the country's size and income level, logistics infrastructure, and the quality of national statistics.
Keywords: C55; C82; E01; E37; R12; 330; night lights; remote sensing; big data; business cycles; leading indicators