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Predicción del IPC mexicano combinando modelos econométricos e inteligencia artificial

Author

Listed:
  • Luis Manuel León Anaya

    (Universidad Autónoma del Estado de México)

  • Víctor Manuel Landassuri Moreno

    (Universidad Autónoma del Estado de México)

  • Héctor Rafael Orozco Aguirre

    (Universidad Autónoma del Estado de México)

  • Maricela Quintana López

    (Universidad Autónoma del Estado de México)

Abstract

El objetivo de este trabajo es descomponer los factores de comportamiento del Índice de Precios y Cotizaciones (IPC) mexicano para ser pronosticado mediante modelos econométricos y redes neuronales artificiales evolutivas. La metodología empleada consiste en reducir la complejidad de análisis y eliminar el ruido en los datos del IPC mediante la descomposición empírica en modos (DEM), combinando las funciones de modo intrínseco (FMIs) resultantes con las variantes de los modelos autorregresivo integrado de promedio móvil (ARIMA) y autorregresivo con heterocedasticidad condicional (ARCH), y el algoritmo de selección de características de programación evolutiva de redes (FS-EPNet) para pronosticar su comportamiento. La configuración experimental y resultados se presentan y analizan mediante tres fases de predicción del IPC. Las limitaciones son que el IPC mexicano no es estacionario, implicando que algunas FMIs tampoco lo sean. La originalidad consiste en la combinación de la DEM con el algoritmo FS-EPNet para analizar la evolución del mercado bursátil mexicano a través de su IPC, con lo cual se demuestra y concluye que genera una mejor predicción que la obtenida a partir de los datos originales.

Suggested Citation

  • Luis Manuel León Anaya & Víctor Manuel Landassuri Moreno & Héctor Rafael Orozco Aguirre & Maricela Quintana López, 2018. "Predicción del IPC mexicano combinando modelos econométricos e inteligencia artificial," 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. 13(4), pages 603-629, Octubre-D.
    • Handle: RePEc:imx:journl:v:13:y:2018:i:4:p:603-629
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    References listed on IDEAS

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    More about this item

    Keywords

    Pronóstico; Índice Bursátil; Series de Tiempo; Descomposición Empírica en Modos; Redes Neuronales Artificiales Evolutivas;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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