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MINIMIZING AVERAGE RISK IN REGRESSION MODELS

Published online by Cambridge University Press:  15 January 2008

Gerda Claeskens  and
Nils Lid Hjort
Gerda Claeskens
Affiliation:
Katholieke Universiteit Leuven
Nils Lid Hjort
Affiliation:
University of Oslo
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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).

Information

Type
Research Article
Information
Econometric Theory , Volume 24 , Issue 2 , April 2008 , pp. 493 - 527
Copyright
© 2008 Cambridge University Press

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