Nowcasting economic activity with electronic payments data: A predictive modeling approach

León-Rincón, Carlos Eduardo; Ortega-Castro, Fabio Gonzalo

Nowcasting economic activity with electronic payments data: A predictive modeling approach

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Borradores de Economía; No. 1037

Date published

2018-02-12

Date

2018-02-12

Authors

León-Rincón, Carlos Eduardo
Ortega-Castro, Fabio Gonzalo

Document language

eng

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Las opiniones contenidas en el presente documento son responsabilidad exclusiva de los autores y no comprometen al Banco de la República ni a su Junta Directiva.

The opinions contained in this document are the sole responsibility of the author and do not commit Banco de la República or its Board of Directors.

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 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 nonlinear 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 reduces 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.