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Model economic phenomena with CART and Random Forest algorithms

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  • Benjamin David

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," EconomiX Working Papers 2017-46, University of Paris Nanterre, EconomiX.
    • Handle: RePEc:drm:wpaper:2017-46
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    File URL: https://economix.fr/pdf/dt/2017/WP_EcoX_2017-46.pdf
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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.
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    3. Aghion, Philippe & Howitt, Peter, 1992. "A Model of Growth through Creative Destruction," Econometrica, Econometric Society, vol. 60(2), pages 323-351, March.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. 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.
    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.
    7. Virginie Coudert & Valérie Mignon & Alexis Penot, 2008. "Oil Price and the Dollar," Post-Print halshs-00353404, HAL.
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    Cited by:

    1. Santiago Rossi & Fernando Toledo, 2022. "Estimation and prediction of current account deficit adjustment dynamics," Ensayos Económicos, Central Bank of Argentina, Economic Research Department, vol. 1(80), pages 100-139, November.

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    More about this item

    Keywords

    decision trees; CART; Random Forest;
    All these keywords.

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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