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Predicción de inflación en Argentina con métodos econométricos clásicos y machine learning

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  • Aguilar Rafael

Abstract

Argentina está en un régimen de alta inflación. Para los policy makers y el resto de los agentes económicos, es crucial contar con predicciones confiables de la inflación. Un hándicap de los modelos predictivos clásicos es que al aumentar la cantidad de variables, también aumenta la varianza de las predicciones. El desarrollo de técnicas de machine learning permite modelar con decenas o centenas de variables y aprovechar la amplitud de datos disponible. En base a trabajos de D’Amato et al. (2018) y Silva Araujo y Piazza Gaglianone (2023), desarrollamos un set de modelos para predecir la inflación argentina entre 2016 y 2024 y comparamos su poder predictivo en distintos horizontes temporales. El ejercicio incluye (i) un modelo ARIMA de serie de tiempo univariado, (ii) modelos de autorregresión vectorial VAR y (iii) regresiones LASSO y ELASTIC NET con elementos de machine learning. Utilizamos como benchmark la mediana de la encuesta REM del BCRA. Encontramos un mejor desempeño de los modelos con machine learning en ambos horizontes. Entre las variables seleccionadas por las regresiones LASSO y ELASTIC NET se destacan la inflación rezagada, el tipo de cambio oficial, los salarios nominales, el ITCRM-BCRA y agregados monetarios como los depósitos y préstamos del sector privado.

Suggested Citation

  • Aguilar Rafael, 2024. "Predicción de inflación en Argentina con métodos econométricos clásicos y machine learning," Asociación Argentina de Economía Política: Working Papers 4704, Asociación Argentina de Economía Política.
  • Handle: RePEc:aep:anales:4704
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    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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