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oai:riuma.uma.es:10630/366462026-02-03T10:59:36Zcom_10630_2254col_10630_37953

Quantum Computing and Deep Learning Methods for GDP Growth Forecasting. Alaminos Aguilera, David Salas-Compás, María Belén Fernández-Gámez, Manuel Ángel Macroeconomía Producto interior bruto Aprendizaje automático (Inteligencia artificial) Computación cuántica https://openpolicyfinder.jisc.ac.uk/id/publication/16642 Precise macroeconomic forecasting is one of the major aims of economic analysis because it facilitates a timely assessment of future economic conditions and can be used for monetary, fiscal, and economic policy purposes. Numerous works have studied the behavior of the macroeconomic situation and have developed models to forecast them. However, the existing models have limitations, and the literature demands more research on the subject given that the accuracy of the models is still poor, and they have only been expanded for developed countries. This paper presents a comparison of methodologies for GDP growth forecasting and, consequently, new forecasting models of GDP growth have been constructed with the ability to estimate accurately future scenarios globally. A sample of 70 countries was used, which has allowed the use of sample combinations that consider the regional heterogeneity of the warning indicators. To the sample under study, different methods have been applied to achieve a high accuracy model, comparing Quantum Computing with Deep Learning procedures, being Deep Neural Decision Trees, which has provided excellent prediction results thanks to large-scale processing with mini-batch-based learning and can be connected to any larger Neural Networks model. Our model has a great potential impact on the adequacy of macroeconomic policy, providing tools that help to achieve macroeconomic and monetary stability at the global level, and creating new methodological opportunities for GDP growth forecasting. 2025-01-21T12:09:21Z 2025-01-21T12:09:21Z 2022 journal article Alaminos, D., Salas, M.B. & Fernández-Gámez, M.A. Quantum Computing and Deep Learning Methods for GDP Growth Forecasting. Comput Econ 59, 803–829 (2022). https://doi.org/10.1007/s10614-021-10110-z https://hdl.handle.net/10630/36646 10.1007/s10614-021-10110-z eng open access Springer Nature