2026-05-28T17:41:45Zhttps://riuma.uma.es/rest/oai/requestoai:riuma.uma.es:10630/366462026-02-03T10:59:36Zcom_10630_2254col_10630_37953
RIUMA. Repositorio Institucional de la Universidad de Málaga
author
Alaminos Aguilera, David
author
Salas-Compás, María Belén
author
Fernández-Gámez, Manuel Ángel
2025-01-21T12:09:21Z
2025-01-21T12:09:21Z
2022
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
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.
eng
Macroeconomía
Producto interior bruto
Aprendizaje automático (Inteligencia artificial)
Computación cuántica
Quantum Computing and Deep Learning Methods for GDP Growth Forecasting.
journal article
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