Statistical methods for modelling neural networks
AbstractIn this paper modelling time series by single hidden layer feedforward neural network models is considered. A coherent modelling strategy based on statistical inference is discussed. The problems of selecting the variables and the number of hidden units are solved by using statistical model selection criteria and tests. Misspecification tests for evaluating an estimated neural network model are considered. Forecasting with neural network models is discussed and an application to a real time series is presented.
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Bibliographic InfoPaper provided by Department of Economics PUC-Rio (Brazil) in its series Textos para discussão with number 445.
Length: 24 pages
Date of creation: Sep 2001
Date of revision:
Publication status: Published in Intelligent Systems, v.9, p. 227-235, 2001
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This paper has been announced in the following NEP Reports:
- NEP-ALL-2001-09-26 (All new papers)
- NEP-CMP-2001-09-26 (Computational Economics)
- NEP-ECM-2001-09-26 (Econometrics)
- NEP-ENT-2001-09-26 (Entrepreneurship)
- NEP-EVO-2001-09-26 (Evolutionary Economics)
- NEP-NET-2001-09-26 (Network Economics)
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