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Model economic phenomena with CART and Random Forest algorithms

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  • Benjamin David

    (EconomiX - EconomiX - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique)

Abstract

The aim of this paper is to highlight the advantages of algorithmic methods for economic research with quantitative orientation. We describe four typical problems involved in econometric modeling, namely the choice of explanatory variables, a functional form, a probability distribution and the inclusion of interactions in a model. We detail how those problems can be solved by using "CART" and "Random Forest" algorithms in a context of massive increasing data availability. We base our analysis on two examples, the identification of growth drivers and the prediction of growth cycles. More generally, we also discuss the application fields of these methods that come from a machine-learning framework by underlining their potential for economic applications.

Suggested Citation

  • Benjamin David, 2017. "Model economic phenomena with CART and Random Forest algorithms," Working Papers hal-04141619, HAL.
    • Handle: RePEc:hal:wpaper:hal-04141619
    Note: View the original document on HAL open archive server: https://hal.science/hal-04141619
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    References listed on IDEAS

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    1. Coudert, Virginie & Mignon, Valérie, 2016. "Reassessing the empirical relationship between the oil price and the dollar," Energy Policy, Elsevier, vol. 95(C), pages 147-157.
    2. Aghion, Philippe & Howitt, Peter, 1992. "A Model of Growth through Creative Destruction," Econometrica, Econometric Society, vol. 60(2), pages 323-351, March.
    3. Archer, Kellie J. & Kimes, Ryan V., 2008. "Empirical characterization of random forest variable importance measures," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2249-2260, January.
    4. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    5. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    6. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
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