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Anders, Ulrich
Korn, Olaf
Year of Publication: 
Series/Report no.: 
ZEW Discussion Papers 96-21
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.
Neural Networks
Statistical Inference
Model Selection
Information Criteria
Cross Validation
Document Type: 
Working Paper
Document Version: 
Digitized Version

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