Econom\'etrie et Machine Learning

TL;DR

This paper explores the relationship between econometrics and machine learning, highlighting their differences, recent convergence, and the potential for integrating machine learning tools into econometric modeling.

Contribution

It provides an overview of the contrasting cultures of econometrics and machine learning and discusses how their integration can enhance predictive modeling in economics.

Findings

Machine learning models outperform traditional econometric techniques in prediction.

Recent developments show increased convergence between econometrics and machine learning.

Integration of these fields can improve handling of large datasets in economics.

Abstract

Econometrics and machine learning seem to have one common goal: to construct a predictive model, for a variable of interest, using explanatory variables (or features). However, these two fields developed in parallel, thus creating two different cultures, to paraphrase Breiman (2001). The first was to build probabilistic models to describe economic phenomena. The second uses algorithms that will learn from their mistakes, with the aim, most often to classify (sounds, images, etc.). Recently, however, learning models have proven to be more effective than traditional econometric techniques (with a price to pay less explanatory power), and above all, they manage to manage much larger data. In this context, it becomes necessary for econometricians to understand what these two cultures are, what opposes them and especially what brings them closer together, in order to appropriate tools developed by the statistical learning community to integrate them into Econometric models.