Model economic phenomena with CART and Random Forest algorithms
No 2017-46, EconomiX Working Papers from University of Paris Nanterre, EconomiX
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.
Keywords: decision trees; CART; Random Forest (search for similar items in EconPapers)
JEL-codes: C4 C18 C38 C55 (search for similar items in EconPapers)
Pages: 32 pages
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ecm
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Persistent link: https://EconPapers.repec.org/RePEc:drm:wpaper:2017-46
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