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Comparing variable selection techniques for linear regression: LASSO and Autometrics

  • Camila Epprecht

    ()

    (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Paris I - Panthéon-Sorbonne, Pontifical Catholic University of Rio de Janeiro - Department of Electrical Engineering)

  • Dominique Guegan

    ()

    (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Paris I - Panthéon-Sorbonne)

  • Álvaro Veiga

    ()

    (Pontifical Catholic University of Rio de Janeiro - Department of Electrical Engineering)

In this paper, we compare two different variable selection approaches for linear regression models: Autometrics (automatic general-to-specific selection) and LASSO (ℓ1-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.

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Paper provided by HAL in its series Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) with number halshs-00917797.

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Date of creation: Nov 2013
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Handle: RePEc:hal:cesptp:halshs-00917797
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