Multi-Country and Multi-Horizon GDP Forecasting Using Temporal Fusion Transformers

Juan Laborda (), Sonia Ruano and Ignacio Zamanillo
Additional contact information
Juan Laborda: Department of Business Administration, Universidad Carlos III, Getafe, 28903 Madrid, Spain
Sonia Ruano: Banco de España, C/Alcalá 48, 28014 Madrid, Spain
Ignacio Zamanillo: Banco de España, C/Alcalá 48, 28014 Madrid, Spain

Mathematics, 2023, vol. 11, issue 12, 1-26

Abstract: This paper applies a new artificial intelligence architecture, the temporal fusion transformer (TFT), for the joint GDP forecasting of 25 OECD countries at different time horizons. This new attention-based architecture offers significant advantages over other deep learning methods. First, results are interpretable since the impact of each explanatory variable on each forecast can be calculated. Second, it allows for visualizing persistent temporal patterns and identifying significant events and different regimes. Third, it provides quantile regressions and permits training the model on multiple time series from different distributions. Results suggest that TFTs outperform regression models, especially in periods of turbulence such as the COVID-19 shock. Interesting economic interpretations are obtained depending on whether the country has domestic demand-led or export-led growth. In essence, TFT is revealed as a new tool that artificial intelligence provides to economists and policy makers, with enormous prospects for the future.

Keywords: GDP; deep learning; time fusion transformers; multi-horizon forecasting; interpretability (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/12/2625/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/12/2625/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:12:p:2625-:d:1166929

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().