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Quantum Computing and Deep Learning Methods for GDP Growth Forecasting

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Abstract

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.

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Change history

  • 25 March 2021

    on page 1, the corresponding author's affiliation “Mangement” included for blind review was replaced with “Management”

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Authors and Affiliations

  1. Department of Financial Management, Universidad Pontificia Comillas, Madrid, Spain

    David Alaminos

  2. PhD Program in Economics and Business, Universidad de Málaga, Málaga, Spain

    M. Belén Salas

  3. Department of Finance and Accounting, Universidad de Málaga, Málaga, Spain

    Manuel A. Fernández-Gámez

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  1. David Alaminos
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Appendix

Appendix

See Tables 5, 6 and 7.

Table 5 Sample of emerging countries
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Table 6 Sample of emerging countries
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Table 7 Global sample
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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

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