Variable selection and forecasting via automated methods for linear models: LASSO/adaLASSO and Autometrics

Camila Epprecht (), Dominique Guegan (), Álvaro Veiga () and Joel Correa da Rosa ()
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Camila Epprecht: Centre d'Economie de la Sorbonne and Department of Electrical Engineering-Pontifical Catholic University of Rio de Janeiro, https://centredeconomiesorbonne.cnrs.fr
Dominique Guegan: Centre d'Economie de la Sorbonne - Paris School of Economics, LabEX ReFi and IPAG Paris, https://cv.archives-ouvertes.fr/dominique-guegan
Álvaro Veiga: Department of Electrical Engineering-Pontifical Catholic University of Rio de Janeiro
Joel Correa da Rosa: Icahn School of Medicine at Mount Sinai - Department of Population Health Science & Policy, New York

Documents de travail du Centre d'Economie de la Sorbonne from Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne

Abstract: In this paper we compare two approaches of model selection methods for linear regression models: classical approach - Autometrics (automatic general-to-specific selection) — and statistical learning - LASSO (?1-norm regularization) and adaLASSO (adaptive LASSO). In a simulation experiment, considering a simple setup with orthogonal candidate variables and independent data, we compare the performance of the methods concerning predictive power (out-of-sample forecast), selection of the correct model (variable selection) and parameter estimation. The case where the number of candidate variables exceeds the number of observation is considered as well. Finally, in an application using genomic data from a highthroughput experiment we compare the predictive power of the methods to predict epidermal thickness in psoriatic patients

Keywords: Model selection; general-to-specific; adaptive LASSO; sparse models; Monte Carlo simulation; genetic data (search for similar items in EconPapers)
JEL-codes: C51 C52 C53 (search for similar items in EconPapers)
Pages: 27 pages
Date: 2013-11, Revised 2017-10
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:mse:cesdoc:13080r

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