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    • Revista IJIMAI
    • 2022
    • vol. 7, nº 3, march 2022
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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2022
    • vol. 7, nº 3, march 2022
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    The Yield Curve as a Recession Leading Indicator. An Application for Gradient Boosting and Random Forest

    Autor: 
    Cadahia Delgado, Pedro
    ;
    Congregado, Emilio
    ;
    Golpe, Antonio A.
    ;
    Vides, José Carlos
    Fecha: 
    03/2022
    Palabra clave: 
    gradient boosting machine; random forest; rules detection; treasury yield curve; IJIMAI
    Revista / editorial: 
    International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)
    Tipo de Ítem: 
    article
    URI: 
    https://reunir.unir.net/handle/123456789/13140
    DOI: 
    https://doi.org/10.9781/ijimai.2022.02.006
    Dirección web: 
    https://www.ijimai.org/journal/bibcite/reference/3108
    Open Access
    Resumen:
    Most representative decision-tree ensemble methods have been used to examine the variable importance of Treasury term spreads to predict US economic recessions with a balance of generating rules for US economic recession detection. A strategy is proposed for training the classifiers with Treasury term spreads data and the results are compared in order to select the best model for interpretability. We also discuss the use of SHapley Additive exPlanations (SHAP) framework to understand US recession forecasts by analyzing feature importance. Consistently with the existing literature we find the most relevant Treasury term spreads for predicting US economic recession and a methodology for detecting relevant rules for economic recession detection. In this case, the most relevant term spread found is 3-month–6-month, which is proposed to be monitored by economic authorities. Finally, the methodology detected rules with high lift on predicting economic recession that can be used by these entities for this propose. This latter result stands in contrast to a growing body of literature demonstrating that machine learning methods are useful for interpretation comparing many alternative algorithms and we discuss the interpretation for our result and propose further research lines aligned with this work.
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