RT Journal Article
T1 Quantum Computing and Deep Learning Methods for  GDP Growth Forecasting.
A1 Alaminos Aguilera, David
A1 Salas-Compás, María Belén
A1 Fernández-Gámez, Manuel Ángel
K1 Macroeconomía
K1 Producto interior bruto
K1 Aprendizaje automático (Inteligencia artificial)
K1 Computación cuántica
AB Precise macroeconomic forecasting is one of the major aims of economic analysisbecause it facilitates a timely assessment of future economic conditions and can be usedfor monetary, fiscal, and economic policy purposes. Numerous works have studied thebehavior of the macroeconomic situation and have developed models to forecast them.However, the existing models have limitations, and the literature demands more researchon the subject given that the accuracy of the models is still poor, and they have only beenexpanded for developed countries. This paper presents a comparison of methodologiesfor GDP growth forecasting and, consequently, new forecasting models of GDP growthhave been constructed with the ability to estimate accurately future scenarios globally. Asample of 70 countries was used, which has allowed the use of sample combinations thatconsider the regional heterogeneity of the warning indicators. To the sample under study,different methods have been applied to achieve a high accuracy model, comparingQuantum Computing with Deep Learning procedures, being Deep Neural Decision Trees,which has provided excellent prediction results thanks to large-scale processing withmini-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 globallevel, and creating new methodological opportunities for GDP growth forecasting.
PB Springer Nature
YR 2022
FD 2022
LK https://hdl.handle.net/10630/36646
UL https://hdl.handle.net/10630/36646
LA eng
NO 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
NO https://openpolicyfinder.jisc.ac.uk/id/publication/16642
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026