Unité mixte de recherche 7235

Model economic phenomena with CART and Random Forest algorithms

Benjamin David

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.

AGENDA

jeudi 1 juin 2023

Lunch

Quentin Hoarau

Feebate on new vehicles : pass-through on the second-hand market and distributional impacts

vendredi 2 juin 2023

Rencontres économiques

9h30 à 11h30

Le renouveau industriel français est-il encore possible ?

jeudi 8 juin 2023

Doctorants

Sahil Chopra (Université Sorbonne Paris-Nord)

Economics of litigation : Securities class action with third-party funding

lundi 12 juin 2023

Law, Institutions and Economics in Nanterre (LIEN)

Arthur Silve (IAST / Univ. Laval)

TBA

jeudi 15 juin 2023

Lunch

Guillaume Pierné

TBA

lundi 19 juin 2023

Law, Institutions and Economics in Nanterre (LIEN)

Juan Mora-Sanguinetti (Banco de Espana)

TBA

jeudi 22 juin 2023

Doctorants

Jules Chaperon

TBA

vendredi 1 septembre 2023

Professeurs invités

Ken Yahagi

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