Arbeitspapier
Forecasting with artificial network models
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: SSE/EFI Working Paper Series in Economics and Finance ; No. 491
- Klassifikation
-
Wirtschaft
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Forecasting Models; Simulation Methods
- Thema
-
Neural networks
forecasting
nonlinear time series
Neuronale Netze
Schätztheorie
Theorie
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Rech, Gianluigi
- Ereignis
-
Veröffentlichung
- (wer)
-
Stockholm School of Economics, The Economic Research Institute (EFI)
- (wo)
-
Stockholm
- (wann)
-
2002
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Rech, Gianluigi
- Stockholm School of Economics, The Economic Research Institute (EFI)
Entstanden
- 2002
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