European Business Schools Librarian's Group

SSE/EFI Working Paper Series in Economics and Finance,
Stockholm School of Economics

No 491: Forecasting with artificial neural network models

Gianluigi Rech
Additional contact information
Gianluigi Rech: QA Analysis, ELECTRABEL, Place de l'Universite', 16, LLN, B-1348 Belgium, Postal: Stockholm School of Economics, P.O. Box 6501, SE-113 83 Stockholm, Sweden

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.

Keywords: Neural networks; forecasting; nonlinear time series

JEL-codes: C22; C53

35 pages, February 11, 2002

Full text files 

hastef0491.pdf PDF-file Full text

Download statistics

Questions (including download problems) about the papers in this series should be directed to Helena Lundin ()
Report other problems with accessing this service to Sune Karlsson ().

RePEc:hhs:hastef:0491This page generated on 2024-09-13 22:19:41.