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Minimizing Average Risk In Regression Models

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  • Claeskens, Gerda
  • Hjort, Nils Lid

Abstract

Most model selection mechanisms work in an “overall” modus, providing models without specific concern for how the selected model is going to be used afterward. The focused information criterion (FIC), on the other hand, is geared toward optimum model selection when inference is required for a given estimand. In this paper the FIC method is extended to weighted versions. This allows one to rank and select candidate models for the purpose of handling a range of similar tasks well, as opposed to being forced to focus on each task separately. Applications include selecting regression models that perform well for specified regions of covariate values. We derive these weighted focused information criteria (wFIC), give asymptotic results, and apply the methods to real data. Formulas for easy implementation are provided for the class of generalized linear models.We express our sincere thanks to all reviewers of this paper, including the special issue guest editors and editor Professor Phillips, whose comments and questions have contributed to significant improvements. We also thank Dr. Ronald Klein for kindly giving permission to use the WESDR data. The work of Claeskens has been supported in part by the Fund for Scientific Research Flanders (G.0542.06).

Suggested Citation

  • Claeskens, Gerda & Hjort, Nils Lid, 2008. "Minimizing Average Risk In Regression Models," Econometric Theory, Cambridge University Press, vol. 24(2), pages 493-527, April.
    • Handle: RePEc:cup:etheor:v:24:y:2008:i:02:p:493-527_08
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    Cited by:

    1. Manuel Frondel & Peter Behl & Holger Dette & Harald Tauchmann, 2011. "Choice is Suffering: A Focused Information Criterion for Model Selection Activation Program for Disadvantaged Youths," Ruhr Economic Papers 0250, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    2. Minsu Chang & Francis J. DiTraglia, 2020. "A Generalized Focused Information Criterion for GMM," Papers 2011.07085, arXiv.org.
    3. Behl, Peter & Dette, Holger & Frondel, Manuel & Vance, Colin, 2019. "A focused information criterion for quantile regression: Evidence for the rebound effect," The Quarterly Review of Economics and Finance, Elsevier, vol. 71(C), pages 223-227.
    4. DiTraglia, Francis J., 2016. "Using invalid instruments on purpose: Focused moment selection and averaging for GMM," Journal of Econometrics, Elsevier, vol. 195(2), pages 187-208.
    5. Behl, Peter & Dette, Holger & Frondel, Manuel & Tauchmann, Harald, 2012. "Choice is suffering: A Focused Information Criterion for model selection," Economic Modelling, Elsevier, vol. 29(3), pages 817-822.
    6. Behl, Peter & Dette, Holger & Frondel, Manuel & Tauchmann, Harald, 2013. "Energy substitution: When model selection depends on the focus," Energy Economics, Elsevier, vol. 39(C), pages 233-238.
    7. Xinyu Zhang & Alan T. K. Wan & Sherry Z. Zhou, 2011. "Focused Information Criteria, Model Selection, and Model Averaging in a Tobit Model With a Nonzero Threshold," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 132-142, June.
    8. Francis J. DiTraglia, 2011. "Using Invalid Instruments on Purpose: Focused Moment Selection and Averaging for GMM, Second Version," PIER Working Paper Archive 14-045, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 09 Dec 2014.
    9. Francis DiTraglia, 2011. "Using Invalid Instruments on Purpose: Focused Moment Selection and Averaging for GMM, Second Version," PIER Working Paper Archive 15-027, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 10 Aug 2015.
    10. Schomaker, Michael & Wan, Alan T.K. & Heumann, Christian, 2010. "Frequentist Model Averaging with missing observations," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3336-3347, December.
    11. repec:zbw:rwirep:0250 is not listed on IDEAS

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