Laborda, J.; Ruano, S.; Zamanillo, I. Multi-Country and Multi-Horizon GDP Forecasting Using Temporal Fusion Transformers. Mathematics2023, 11, 2625.
Laborda, J.; Ruano, S.; Zamanillo, I. Multi-Country and Multi-Horizon GDP Forecasting Using Temporal Fusion Transformers. Mathematics 2023, 11, 2625.
Laborda, J.; Ruano, S.; Zamanillo, I. Multi-Country and Multi-Horizon GDP Forecasting Using Temporal Fusion Transformers. Mathematics2023, 11, 2625.
Laborda, J.; Ruano, S.; Zamanillo, I. Multi-Country and Multi-Horizon GDP Forecasting Using Temporal Fusion Transformers. Mathematics 2023, 11, 2625.
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 to visualize persistent temporal patterns and to identify significant events and different regimes. Third, it provides quantile regressions and permits to train 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 is 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
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
