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Comparing variable selection techniques for linear regression: LASSO and Autometrics

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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

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  • Camila Epprecht & Dominique Guegan & Álvaro Veiga, 2013. "Comparing variable selection techniques for linear regression: LASSO and Autometrics," Documents de travail du Centre d'Economie de la Sorbonne 13080, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    • Handle: RePEc:mse:cesdoc:13080
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    More about this item

    Keywords

    Model selection; variable selection; GETS; Autometrics; LASSO; adaptive LASSO; sparse models; oracle property; time series; GDP forecasting;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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