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: CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, PUC-Rio - Pontifícia Universidade Católica do Rio de Janeiro [Brasil] = Pontifical Catholic University of Rio de Janeiro [Brazil] = Université catholique pontificale de Rio de Janeiro [Brésil]
Dominique Guegan: CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique
Álvaro Veiga: PUC-Rio - Pontifícia Universidade Católica do Rio de Janeiro [Brasil] = Pontifical Catholic University of Rio de Janeiro [Brazil] = Université catholique pontificale de Rio de Janeiro [Brésil]
Joel Correa da Rosa: MSSM - Icahn School of Medicine at Mount Sinai [New York]

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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 (ℓ1-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)
Date: 2017-10
New Economics Papers: this item is included in nep-for
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00917797v2
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Published in 2017

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