Pronóstico de la inflación en tiempos de estabilidad y crisis: un enfoque con Machine Learning

Mauricio Mora Barrenechea

doi:10.35319/lajed.202544578

Autores/as

Mauricio Mora Barrenechea https://orcid.org/0009-0007-4046-4482

DOI:

https://doi.org/10.35319/lajed.202544578

Palabras clave:

Machine Learning, inflación, proyecciones, crisis

Resumen

La economía boliviana atraviesa su crisis más grave desde la década de 1980, marcada por una drástica transición de una inflación baja y estable a presiones inflacionarias pronunciadas. En este contexto, el desarrollo de herramientas de pronóstico fiables se ha vuelto cada vez más crucial. Este estudio evalúa el rendimiento predictivo de varios modelos de Machine Learning (ML) ampliamente utilizados en dos condiciones macroeconómicas distintas: periodos de relativa estabilidad y periodos de crisis. Los resultados revelan que, en general, los modelos de ML superan a los enfoques econométricos tradicionales en ambas condiciones, siendo el algoritmo XGBoost el que demuestra un rendimiento más destacable. Además, se observó que la incorporación de un conjunto más amplio de indicadores macroeconómicos mejora la precisión del pronóstico. Estos resultados sugieren que las técnicas de ML pueden servir como complementos valiosos para los modelos econométricos en el pronóstico macroeconómico, especialmente en entornos complejos como el de Bolivia.

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Biografía del autor/a

Mauricio Mora Barrenechea

Investigador Independiente.

Licenciado en Economía y Máster en Preparación, Evaluación Social y Gestión de Proyectos ambos por la Universidad Católica Boliviana San Pablo. Actualmente, cursa estudios en Data Engineering and Analytics en el Canadian College of Technology and Business. Cuenta con amplia experiencia en investigación económica empírica, especializada principalmente en análisis de dinámica inflacionaria, impactos de shocks externos en economías pequeñas y abiertas, y evaluación de política monetaria. Ha desempeñado funciones de análisis, modelación, preparación de proyecciones de corto y largo plazo, y asesoría técnica en el ámbito macroeconómico.

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