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A comparative study of the Lasso-type and heuristic model selection methods

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  • Ivan Savin

Abstract

This study presents a first comparative analysis of Lasso-type (Lasso, adaptive Lasso, elastic net) and heuristic subset selection methods. Although the Lasso has shown success in many situations, it has some limitations. In particular, inconsistent results are obtained for pairwise strongly correlated predictors. An alternative to the Lasso is constituted by model selection based on information criteria (IC), which remains consistent in the situation mentioned. However, these criteria are hard to optimize due to a discrete search space. To overcome this problem, an optimization heuristic (Genetic Algorithm) is applied. Monte-Carlo simulation results are reported to illustrate the performance of the methods.

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Bibliographic Info

Paper provided by COMISEF in its series Working Papers with number 042.

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Length: 28 pages
Date of creation: 24 Aug 2010
Date of revision:
Handle: RePEc:com:wpaper:042

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Web page: http://www.comisef.eu

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Keywords: Model selection; Lasso; adaptive Lasso; elastic net; heuristic methods; genetic algorithms;

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  1. Sin, Chor-Yiu & White, Halbert, 1996. "Information criteria for selecting possibly misspecified parametric models," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 207-225.
  2. Ulrike Schneider & Martin Wagner, 2012. "Catching Growth Determinants with the Adaptive Lasso," German Economic Review, Verein für Socialpolitik, vol. 13(1), pages 71-85, 02.
  3. Kapetanios, George & Labhard, Vincent & Price, Simon, 2008. "Forecasting Using Bayesian and Information-Theoretic Model Averaging: An Application to U.K. Inflation," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 33-41, January.
  4. Kapetanios, George, 2007. "Variable selection in regression models using nonstandard optimisation of information criteria," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 4-15, September.
  5. David F. Hendry & Hans-Martin Krolzig, 2005. "The Properties of Automatic "GETS" Modelling," Economic Journal, Royal Economic Society, vol. 115(502), pages C32-C61, 03.
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  7. Jianqing Fan & Jinchi Lv, 2008. "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 849-911.
  8. Carmen Fernandez & Eduardo Ley & Mark Steel, 1999. "Model uncertainty in cross-country growth regressions," Econometrics 9903003, EconWPA, revised 06 Oct 2001.
  9. Mehmet Caner, 2006. "A lasso type gmm estimator," Working Papers 210, University of Pittsburgh, Department of Economics, revised Jan 2006.
  10. Scott Foster & Arūnas Verbyla & Wayne Pitchford, 2008. "A random model approach for the LASSO," Computational Statistics, Springer, vol. 23(2), pages 217-233, April.
  11. Eduardo Acosta-González & Fernando Fernández-Rodríguez, 2007. "Model selection via genetic algorithms illustrated with cross-country growth data," Empirical Economics, Springer, vol. 33(2), pages 313-337, September.
  12. Ivan Savin & Peter Winker, 2012. "Heuristic Optimization Methods for Dynamic Panel Data Model Selection: Application on the Russian Innovative Performance," Computational Economics, Society for Computational Economics, vol. 39(4), pages 337-363, April.
  13. Sala-i-Martin, Xavier, 1997. "I Just Ran Two Million Regressions," American Economic Review, American Economic Association, vol. 87(2), pages 178-83, May.
  14. Peter Winker & Dietmar Maringer, 2009. "The convergence of estimators based on heuristics: theory and application to a GARCH model," Computational Statistics, Springer, vol. 24(3), pages 533-550, August.
  15. Ivan Savin & Peter Winker, 2012. "Lasso-type and Heuristic Strategies in Model Selection and Forecasting," Jena Economic Research Papers 2012-055, Friedrich-Schiller-University Jena, Max-Planck-Institute of Economics.
  16. Artin Armagan & Russell Zaretzki, 2010. "Model selection via adaptive shrinkage with t priors," Computational Statistics, Springer, vol. 25(3), pages 441-461, September.
  17. Dorsey, Robert E & Mayer, Walter J, 1995. "Genetic Algorithms for Estimation Problems with Multiple Optima, Nondifferentiability, and Other Irregular Features," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 53-66, January.
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Cited by:
  1. Ivan Savin & Peter Winker, 2010. "Heuristic Optimization Methods for Dynamic Panel Data Model Selection. Application on the Russian Innovative Performance," Working Papers 027, COMISEF.

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