Short-term real-time forecasting model for Spanish GDP (Spain-STING): new specification and reassessment of its predictive power

Series: Occasional Papers. 2406.

Author: Ana Gómez Loscos, Miguel Ángel González Simón and Matías José Pacce.

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Short-term real-time forecasting model for Spanish GDP (Spain-STING): new specification and reassessment of its predictive power (769 KB)

Summary

After the outbreak of the COVID-19 pandemic, most economic indicators experienced an increase in the observed volatility, which reduced the accuracy of nowcasting models. In this paper, we present a revision of the Spain-STING model – one of the tools used by the Banco de España to nowcast the quarterly GDP growth rate – that focuses on improving the model’s predictive ability after the pandemic. Specifically, three main changes are made: (i) the relationship between the indicators and the estimated common factor is now contemporaneous, and not leading for some of the indicators; (ii) the variance of the common component is estimated by a stochastic process to allow it to vary over time; (iii) the set of variables is revised with the aim of including only those that add the most relevant information to the nowcast of the quarterly GDP growth rate after the pandemic. All three modifications imply a notable improvement in the Spain-STING nowcasting performance during the post-pandemic period, while maintaining its pre-pandemic accuracy.