Model economic phenomena with CART and Random Forest algorithms
Year: 2017
Type: preprint
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.
Author: Benjámin Dávid
Institution: EconomiX
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2778280043