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Choice is suffering: A Focused Information Criterion for model selection

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Author Info

  • Behl, Peter
  • Dette, Holger
  • Frondel, Manuel
  • Tauchmann, Harald

Abstract

In contrast to conventional measures, the Focused Information Criterion (FIC) allows the purpose-specific selection of models, thereby reflecting the idea that one kind of model might be appropriate for inferences on a parameter of interest, but not for another. Ever since its invention, the FIC has been increasingly applied in the realm of statistics, but this concept appears to be virtually unknown in the economic literature. Using a straightforward analytical example, this paper provides for a didactic illustration of the FIC and demonstrates its usefulness in economic applications.

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File URL: http://www.sciencedirect.com/science/article/pii/S0264999311002203
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Bibliographic Info

Article provided by Elsevier in its journal Economic Modelling.

Volume (Year): 29 (2012)
Issue (Month): 3 ()
Pages: 817-822

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Handle: RePEc:eee:ecmode:v:29:y:2012:i:3:p:817-822

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Web page: http://www.elsevier.com/locate/inca/30411

Related research

Keywords: Akaike Information Criterion; Schwarz Information Criterion; Translog cost function;

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Cited by:
  1. Peter Behl & Holger Dette & Manuel Frondel & Harald Tauchmann, 2011. "Being Focused: When the Purpose of Inference Matters for Model Selection," Ruhr Economic Papers 0264, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.

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