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Cross-validation for selecting a model selection procedure

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  • Zhang, Yongli
  • Yang, Yuhong

Abstract

While there are various model selection methods, an unanswered but important question is how to select one of them for data at hand. The difficulty is due to that the targeted behaviors of the model selection procedures depend heavily on uncheckable or difficult-to-check assumptions on the data generating process. Fortunately, cross-validation (CV) provides a general tool to solve this problem. In this work, results are provided on how to apply CV to consistently choose the best method, yielding new insights and guidance for potentially vast amount of application. In addition, we address several seemingly widely spread misconceptions on CV.

Suggested Citation

  • Zhang, Yongli & Yang, Yuhong, 2015. "Cross-validation for selecting a model selection procedure," Journal of Econometrics, Elsevier, vol. 187(1), pages 95-112.
    • Handle: RePEc:eee:econom:v:187:y:2015:i:1:p:95-112
    DOI: 10.1016/j.jeconom.2015.02.006
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    References listed on IDEAS

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    1. T. Speed & Bin Yu, 1993. "Model selection and prediction: Normal regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 45(1), pages 35-54, March.
    2. Yuhong Yang, 2005. "Can the strengths of AIC and BIC be shared? A conflict between model indentification and regression estimation," Biometrika, Biometrika Trust, vol. 92(4), pages 937-950, December.
    3. Tim van Erven & Peter Grünwald & Steven de Rooij, 2012. "Catching up faster by switching sooner: a predictive approach to adaptive estimation with an application to the AIC–BIC dilemma," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(3), pages 361-417, June.
    4. Ng, Serena, 2013. "Variable Selection in Predictive Regressions," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 752-789, Elsevier.
    5. Vaart Aad W. van der & Dudoit Sandrine & Laan Mark J. van der, 2006. "Oracle inequalities for multi-fold cross validation," Statistics & Risk Modeling, De Gruyter, vol. 24(3), pages 1-21, December.
    6. Andrews, Donald W. K., 1991. "Asymptotic optimality of generalized CL, cross-validation, and generalized cross-validation in regression with heteroskedastic errors," Journal of Econometrics, Elsevier, vol. 47(2-3), pages 359-377, February.
    7. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    8. Yang Y., 2001. "Adaptive Regression by Mixing," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 574-588, June.
    9. Jianqing Fan & Jinchi Lv & Lei Qi, 2011. "Sparse High-Dimensional Models in Economics," Annual Review of Economics, Annual Reviews, vol. 3(1), pages 291-317, September.
    10. Yang, Yuhong, 2007. "Prediction/Estimation With Simple Linear Models: Is It Really That Simple?," Econometric Theory, Cambridge University Press, vol. 23(1), pages 1-36, February.
    11. Shen X. & Ye J., 2002. "Adaptive Model Selection," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 210-221, March.
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