Neural Networks for Regional Employment Forecasts: Are the Parameters Relevant?
In this paper, we present a review of various computational experiments concerning neural network (NN) models developed for regional employment forecasting. NNs are nowadays widely used in several fields because of their flexible specification structure. A series of NN experiments is presented in the paper, using two data sets on German NUTS-3 districts. Individual forecasts are computed by our models for each district, in order to answer the following question: How relevant are NN parameters in comparison to NN structure? Comprehensive testing of these parameters is limited in the literature. Building on different specifications of NN models – in terms of explanatory variables and NN structures – we propose a systematic choice of NN learning parameters and internal functions by means of a sensitivity analysis. Our results show that different combinations of NN parameters provide significantly varying statistical performance and forecasting power. Finally, we note that the sets of parameters chosen for a given model specification cannot be light-heartedly applied to different or more complex models.
|Date of creation:||Jan 2009|
|Date of revision:||Feb 2010|
|Publication status:||Published in the Journal of Geographical Systems, 13(1):67–85, 2011|
|Contact details of provider:|| Postal: Via Patara, 3, 47921 Rimini (RN)|
Web page: http://www.rcfea.org
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Roberto Patuelli & Daniel A. Griffith & Michael Tiefelsdorf & Peter Nijkamp, 2009.
"Spatial Filtering and Eigenvector Stability: Space-Time Models for German Unemployment Data,"
Quaderni della facoltà di Scienze economiche dell'Università di Lugano
0902, USI Università della Svizzera italiana.
- Roberto Patuelli & Daniel A. Griffith & Michael Tiefelsdorf & Peter Nijkamp, 2011. "Spatial Filtering and Eigenvector Stability: Space-Time Models for German Unemployment Data," International Regional Science Review, SAGE Publishing, vol. 34(2), pages 253-280, April.
- Roberto Patuelli & Daniel A. Griffith & Michael Tiefelsdorf & Peter Nijkamp, 2009. "Spatial Filtering and Eigenvector Stability: Space-Time Models for German Unemployment Data," Working Paper Series 02_09, The Rimini Centre for Economic Analysis, revised May 2010.
- Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Uwe Blien, 2006.
"New Neural Network Methods for Forecasting Regional Employment: An Analysis of German Labour Markets,"
Tinbergen Institute Discussion Papers
06-020/3, Tinbergen Institute.
- Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Uwe Blien, 2006. "New Neural Network Methods for Forecasting Regional Employment: an Analysis of German Labour Markets," Spatial Economic Analysis, Taylor & Francis Journals, vol. 1(1), pages 7-30.
- Kuan, Chung-Ming & Liu, Tung, 1995. "Forecasting Exchange Rates Using Feedforward and Recurrent Neural Networks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(4), pages 347-64, Oct.-Dec..
- Roberto Patuelli & Aura Reggiani & Peter Nijkamp, .
"The Development of Regional Employment in Germany: Results from Neural Network Experiments,"
Regional and Urban Modeling
- Aura Reggiani & Roberto Patuelli & Peter Nijkamp, 2006. "The development of Regional employment in Germany: Results from Neural Network Experiments," SCIENZE REGIONALI, FrancoAngeli Editore, vol. 2006(3).
- Roberto Patuelli & Simonetta Longhi & Aura Reggiani & Peter Nijkamp, 2005.
"Forecasting Regional Employment in Germany by Means of Neural Networks and Genetic Algorithms,"
- Roberto Patuelli & Simonetta Longhi & Aura Reggiani & Peter Nijkamp, 2008. "Neural networks and genetic algorithms as forecasting tools: a case study on German regions," Environment and Planning B: Planning and Design, Pion Ltd, London, vol. 35(4), pages 701-722, July.
- Suahasil Nazara & Geoffrey J.D. Hewings, 2004. "Spatial Structure and Taxonomy of Decomposition in Shift-Share Analysis," Growth and Change, Wiley Blackwell, vol. 35(4), pages 476-490.
- Gorr, Wilpen L. & Nagin, Daniel & Szczypula, Janusz, 1994. "Comparative study of artificial neural network and statistical models for predicting student grade point averages," International Journal of Forecasting, Elsevier, vol. 10(1), pages 17-34, June.
- Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
- repec:dgr:uvatin:20060020 is not listed on IDEAS
When requesting a correction, please mention this item's handle: RePEc:rim:rimwps:07_09. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Marco Savioli)
If references are entirely missing, you can add them using this form.