Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/56171 
Autor:innen: 
Erscheinungsjahr: 
2002
Schriftenreihe/Nr.: 
SSE/EFI Working Paper Series in Economics and Finance No. 491
Verlag: 
Stockholm School of Economics, The Economic Research Institute (EFI), Stockholm
Zusammenfassung: 
This paper contains a forecasting exercise on 30 time series, ranging on several fields, from economy to ecology. The statistical approach to artificial neural networks modelling developed by the author is compared to linear modelling and to other three well-known neural network modelling procedures: Information Criterion Pruning (ICP), Cross-Validation Pruning (CVP) and Bayesian Regularization Pruning (BRP). The findings are that 1) the linear models outperform the artificial neural network models and 2) albeit selecting and estimating much more parsimonious models, the statistical approach stands up well in comparison to other more sophisticated ANN models.
Schlagwörter: 
Neural networks
forecasting
nonlinear time series
JEL: 
C22
C53
Dokumentart: 
Working Paper

Datei(en):
Datei
Größe
339.85 kB





Publikationen in EconStor sind urheberrechtlich geschützt.