Nowcasting Economic Activity with Electronic Payments Data: A Predictive Modeling Approach

DOI:  https://doi.org/10.12804/revistas.urosario.edu.co/economia/a.7205
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Published: Oct 10, 2018
Issue: Vol. 21 No. 2 (2018): julio-diciembre
Section Articles


Keywords: pronóstico , aprendizaje de máquina , red neuronal , pagos minoristas , Red narx. prognóstico , aprendizagem de máquina , rede neuronal , pagamentos minoristas , Rede narx. Forecasting , machine learning , neural networks , retail payments , narx.

Main Article Content

Authors

Carlos León
Fabio Ortega
Abstract

Economic activity nowcasting (i. e., making current-period estimates) is convenient because most traditional measures of economic activity come with substantial lags. We aim at nowcasting ise, a short-term economic activity indicator in Colombia. Inputs are the ise’s lags and a dataset of payments made with electronic transfers and cheques among individuals, firms, and the central government. Under a predictive modeling approach, we employ a non-linear autoregressive exogenous neural network model. Results suggest that our choice of inputs and predictive method enable us to nowcast economic activity with fair accuracy. Also, we validate that electronic payments data significantly reduce the nowcast error of a benchmark non-linear autoregressive neural network model. Nowcasting economic activity from electronic payment instruments data not only contributes to agents’ decision making and economic modeling, but also supports new research paths on how to use retail payments data for appending current models.

Author Biographies

Carlos León

Financial Infrastructure Oversight Department, Banco de la República; CentER, Tilburg University

Fabio Ortega

Financial Infrastructure Oversight Department, Banco de la República
How to Cite
León, C., & Ortega, F. (2018). Nowcasting Economic Activity with Electronic Payments Data: A Predictive Modeling Approach. Revista Economía Del Rosario, 21(2), 381–407. https://doi.org/10.12804/revistas.urosario.edu.co/economia/a.7205
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