Econometrics and Machine Learning

Arthur Charpentier (), Emmanuel Flachaire and Antoine Ly
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Arthur Charpentier: CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique - IP Paris - Institut Polytechnique de Paris - CNRS - Centre National de la Recherche Scientifique
Antoine Ly: UPE - Université Paris-Est

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Abstract: On the face of it, econometrics and machine learning share a common goal: to build a predictive model, for a variable of interest, using explanatory variables (or features). However, the two fields have developed in parallel, thus creating two different cultures. Econometrics set out to build probabilistic models designed to describe economic phenomena, while machine learning uses algorithms capable of learning from their mistakes, generally for classification purposes (sounds, images, etc.). Yet in recent years, learning models have been found to be more effective than traditional econometric methods (the price to pay being lower explanatory power) and are, above all, capable of handling much larger datasets. Given this, econometricians need to understand what the two cultures are, what differentiates them and, above all, what they have in common in order to draw on tools developed by the statistical learning community with a view to incorporating them into econometric models.

Keywords: learning; Big Data; econometrics; modelling; least squares (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (5)

Published in Economie et Statistique / Economics and Statistics, 2018, 505d, pp.147-169. ⟨10.24187/ecostat.2018.505d.1970⟩

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Journal Article: Econometrics and Machine Learning (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02163979

DOI: 10.24187/ecostat.2018.505d.1970

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