D. Panigo, P. Gluzmann, E. Mocskos, Adan Mauri Ungaro, Valentin Mari, Nicolás Monzón
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GlobalSearchRegression.jl: \ Building bridges between Machine Learning and Econometrics in Fat-Data scenarios
The aim of this paper is twofold. The first one is to describe a novel research-project designed for building bridges between machine learning and econometric worlds ( ModelSelection.jl). The second one is to introduce the main characteristics and comparative performance of the first Julia-native all-subset regression algorithm included in GlobalSearchRegression.jl (v1.0.5). As other available alternatives, this algorithm allows researchers to obtain the best model specification among all possible covariate combinations - in terms of user defined information criteria-, but up to 3165 and 197 times faster than STATA and R alternatives, respectively.
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