Pronóstico de la Inflación en Nicaragua: Evidencia Comparativa entre Aprendizaje Automático y Econometría Tradicional
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https://doi.org/10.62407/cvpevd47Palabras clave:
Pronóstico de la Inflación, Econometría Tradicional, Aprendizaje Automático, NicaraguaResumen
La aplicación de técnicas de Aprendizaje Automático (Machine Learning, ML) en el análisis econométrico se ha expandido considerablemente en los últimos años, profundizando el debate sobre cómo pronosticar variables macroeconómicas clave como la inflación. En el caso de Nicaragua, actualmente no existe evidencia de que los métodos de ML se hayan integrado en los marcos de pronóstico de inflación, a pesar del papel central de esta variable en la política monetaria. Este estudio evalúa el rendimiento predictivo de modelos de Econometría Tradicional (TE) y de ML para pronosticar la inflación en Nicaragua durante el período 2006–2024. Se construyó un conjunto de datos que comprende 20 variables explicativas y se entrenaron seis modelos: Regresión Lineal Múltiple (LM), ARIMAX y VAR (TE), junto con Elastic Net, Random Forest (RF) y XGBoost (ML). Los resultados indican que no existen diferencias estadísticamente significativas en la precisión predictiva entre ambos enfoques en el corto y largo plazo. Sin embargo, los modelos de ML identificaron relaciones significativas con los precios del petróleo y de las importaciones que no fueron capturadas por las técnicas tradicionales. Estos hallazgos sugieren que la combinación de enfoques de TE y ML puede mejorar la robustez y la interpretabilidad de las metodologías de pronóstico de inflación.
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