INVESTIGADORES
PANIGO Demian Tupac
artículos
Título:
GlobalSearchRegression.jl: Building bridges between Machine Learning and Econometrics in Fat-Data scenarios
Autor/es:
PANIGO, DEMIAN TUPAC; GLUZMANN, PABLO; MOCSKOS, ESTEBAN; MAURI UNGARO, ADAN; MARI, VALENTIN; MONZON, NICOLÁS
Revista:
Proceedings of the JuliaCon Conferences
Editorial:
MIT - Julia-Lab
Referencias:
Lugar: Boston; Año: 2020 vol. 2 p. 1 - 6
ISSN:
2642-4029
Resumen:
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.
