Comparing variable selection techniques for linear regression: LASSO and Autometrics

Camila Epprecht (), Dominique Guegan () and Álvaro Veiga ()
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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, https://cv.archives-ouvertes.fr/dominique-guegan
Álvaro Veiga: Department of Electrical Engineering-Pontifical Catholic University of Rio de Janeiro

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 different variable selection approaches for linear regression models: Autometrics (automatic general-to-specific selection) and LASSO (?1-norm regularization). In a simulation study, we show the performance of the methods considering the predictive power (forecast out-of-sample) and the selection of the correct model and estimation (in-sample). The case where the number of candidate variables exceeds the number of observation is considered as well. We also analyze the properties of estimators comparing to the oracle estimator. Finally, we compare both methods in an application to GDP forecasting

Keywords: Model selection; variable selection; GETS; Autometrics; LASSO; adaptive LASSO; sparse models; oracle property; time series; GDP forecasting (search for similar items in EconPapers)
JEL-codes: C51 C52 C53 (search for similar items in EconPapers)
Date: 2013-11
New Economics Papers: this item is included in nep-ecm and nep-for
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Citations: View citations in EconPapers (4)

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

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