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Modeling economic growth in pandemic times with machine learning regression algorithms

Author

Listed:
  • Jesús Alejandro Navarro Acosta

    (Universidad Autónoma de Coahuila, México)

  • Valeria Soto Mendoza

    (Universidad Autónoma de Coahuila, México)

  • Laura Policardo

    (Customs and Monopolies Agency)

  • Edgar Javier Sánchez Carrera

    (Universidad Autónoma de Coahuila, México)

Abstract

El objetivo es analizar el contraste de políticas para enfrentar la pandemia de Covid-19 en el desempeño socioeconómico de: Italia, México y Estados Unidos. Metodología: Aplicando técnicas de aprendizaje automático (machine learning, ML) para analizar los efectos socioeconómicos de la pandemia (medidas de contención, tasas de infección, muertes totales, vacunación, etc.) sobre el crecimiento del PIB en esos países. El experimento es que el índice de contingencia referencial de Nueva Zelanda reemplaza el propio índice referencial de cada uno de los países para predecir el PIB, muertes inducidas por Covid-19 y tasa de reproducción de Covid-19. Se muestra que las técnicas de ML son herramientas sólidas para regresiones de resultados múltiples y para escenarios experimentales sobre el impacto socioeconómico de la pandemia de Covid-19. Resultados: Los resultados experimentales revelaron que las técnicas de Árbol de Regresión y Bosque Aleatorio estiman y predicen con éxito los casos de Italia, México y Estados Unidos. Conclusiones: La propuesta es contingencia y vacunación son sin duda exitosas en la lucha contra una pandemia, además de medir los efectos de dichas políticas con el uso de técnicas novedosas como el ML.

Suggested Citation

  • Jesús Alejandro Navarro Acosta & Valeria Soto Mendoza & Laura Policardo & Edgar Javier Sánchez Carrera, 2025. "Modeling economic growth in pandemic times with machine learning regression algorithms," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 20(1), pages 1-33, Enero - M.
    • Handle: RePEc:imx:journl:v:20:y:2025:i:1:a:2
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    References listed on IDEAS

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    Keywords

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    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • I1 - Health, Education, and Welfare - - Health
    • O1 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development
    • O5 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies
    • Y1 - Miscellaneous Categories - - Data: Tables and Charts

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