EconStor >
Stockholm School of Economics >
EFI - The Economic Research Institute, Stockholm School of Economics >
SSE/EFI Working Paper Series in Economics and Finance, EFI - The Economic Research Institute, Stockholm School of Economics >

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10419/56171
  
Title:Forecasting with artificial network models PDF Logo
Authors:Rech, Gianluigi
Issue Date:2002
Series/Report no.:SSE/EFI Working Paper Series in Economics and Finance 491
Abstract: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.
Subjects:Neural networks
forecasting
nonlinear time series
JEL:C22
C53
Document Type:Working Paper
Appears in Collections:SSE/EFI Working Paper Series in Economics and Finance, EFI - The Economic Research Institute, Stockholm School of Economics

Files in This Item:
File Description SizeFormat
342881469.pdf339.85 kBAdobe PDF
No. of Downloads: Counter Stats
Download bibliographical data as: BibTeX
Share on:http://hdl.handle.net/10419/56171

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.