Descripción

High-frequency microdata can significantly enhance the accuracy of nowcasting models for economic activity. This study evaluates the performance of using microdata to nowcast the monthly Chilean activity. We compare models with granular data from electronic invoicing and digital payment systems with conventional univariate and multivariate time series models and leading indicators. For the nowcasts with microdata, we employ SARIMAX specifications and a bottom-up aggregation methodology, complemented with satellite models for specific economic sectors. Our empirical results show a substantial reduction of approximately 34% in root mean square errors (RMSE) for nowcasts of the annual growth of IMACEC (monthly economic activity indicator in Chile) over a 36-month out-of-sample evaluation period. These findings underscore the value of microdata for improving real-time estimates of economic activity, encouraging its integration into nowcasting frameworks.