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Erscheinungsjahr: 
1996
Schriftenreihe/Nr.: 
ZEW Discussion Papers No. 96-21
Verlag: 
Zentrum für Europäische Wirtschaftsforschung (ZEW), Mannheim
Zusammenfassung: 
In this article we examine how model selection in neural networks can be guided by statistical procedures such as hypotheses tests, information criteria and cross validation. The application of these methods in neural network models is discussed, paying attention especially to the identification problems encountered. We then propose five specification strategies based on different statistical procedures and compare them in a simulation study. As the results of the study are promising, it is suggested that a statistical analysis should become an integral part of neural network modelling.
Schlagwörter: 
Neural Networks
Statistical Inference
Model Selection
Identification
Information Criteria
Cross Validation
Dokumentart: 
Working Paper
Dokumentversion: 
Digitized Version

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