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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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