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Nowcasting Economic Activity with Microdata

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  • Diego Vivanco Vargas
  • Camilo Levenier Barría
  • Lissette Briones Molina

Abstract

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.

Suggested Citation

  • Diego Vivanco Vargas & Camilo Levenier Barría & Lissette Briones Molina, 2025. "Nowcasting Economic Activity with Microdata," Working Papers Central Bank of Chile 1059, Central Bank of Chile.
    • Handle: RePEc:chb:bcchwp:1059
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