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Set of More Informative Models

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Part of the Advanced Studies in Theoretical and Applied Econometrics book series (ASTA, volume 16)

Abstract

In this Chapter we survey that group of model selection procedures which have been derived within a decision framework in which there is no a priori information on the parameters. A loss function is explicitly assumed and the different criteria are based on the comparison of the estimated values of the corresponding risk function. Within these procedures we can distinguish two groups. The first one consists of those procedures that derive their risk functions assuming that one of the models is the true model. The second one includes those procedures for which the risk functions are obtained assuming in turn that each model is the true one.

Keywords

Loss Function True Model Risk Function Large Model Decision Framework 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 1989

Authors and Affiliations

  1. 1.University of ZaragozaSpain

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