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

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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 (?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

Suggested Citation

  • Camila Epprecht & Dominique Guegan & Álvaro Veiga & Joel Correa da Rosa, 2013. "Variable selection and forecasting via automated methods for linear models: LASSO/adaLASSO and Autometrics," Documents de travail du Centre d'Economie de la Sorbonne 13080r, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Oct 2017.
    • Handle: RePEc:mse:cesdoc:13080r
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    File URL: ftp://mse.univ-paris1.fr/pub/mse/CES2013/13080R.pdf
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    Cited by:

    1. Loann David Denis Desboulets, 2018. "A Review on Variable Selection in Regression Analysis," Econometrics, MDPI, vol. 6(4), pages 1-27, November.

    More about this item

    Keywords

    Model selection; general-to-specific; adaptive LASSO; sparse models; Monte Carlo simulation; genetic data;
    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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