Neural Networks for Cross-Sectional Employment Forecasts: A Comparison of Model Specifications for Germany
AbstractIn this paper, we present a review of various computational experiments – and consequent results – concerning Neural Network (NN) models developed for regional employment forecasting. NNs are widely used in several fields because of their flexible specification structure. Their utilization in studying/predicting economic variables, such as employment or migration, is justified by the ability of NNs of learning from data, in other words, of finding functional relationships – by means of data – among the economic variables under analysis. A series of NN experiments is presented in the paper. Using two data sets on German NUTS 3 districts (326 and 113 labour market districts in the former West and East Germany, respectively), the results emerging from the implementation of various NN models – in order to forecast variations in full-time employment – are provided and discussed In our approach, single forecasts are computed by the models for each district. Different specifications of the NN models are first tested in terms of: (a) explanatory variables; and (b) NN structures. The average statistical results of simulated out-of-sample forecasts on different periods are summarized and commented on. In addition to variable and structure specification, the choice of NN learning parameters and internal functions is also critical to the success of NNs. Comprehensive testing of these parameters is, however, limited in the literature. A sensitivity analysis is therefore carried out and discussed, in order to evaluate different combinations of NN parameters. The paper concludes with methodological and empirical remarks, as well as with suggestions for future research.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by USI Università della Svizzera italiana in its series Quaderni della facoltà di Scienze economiche dell'Università di Lugano with number 0903.
Length: 15 pages
Date of creation: Feb 2009
Date of revision:
Contact details of provider:
Web page: http://www.library.lu.usi.ch
neural networks; sensitivity analysis; employment forecasts; Germany;
Other versions of this item:
- Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Norbert Schanne, 2011. "Neural networks for regional employment forecasts: are the parameters relevant?," Journal of Geographical Systems, Springer, vol. 13(1), pages 67-85, March.
- Patuelli, R. & Reggiani, A. & Nijkamp, P., 2009. "Neural networks for cross-sectional employment forecasts: a comparison of model specifications for germany," Serie Research Memoranda 0014, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
- Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Norbert Schanne, 2009. "Neural Networks for Regional Employment Forecasts: Are the Parameters Relevant?," Working Paper Series 07_09, The Rimini Centre for Economic Analysis, revised Feb 2010.
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
- E27 - Macroeconomics and Monetary Economics - - Macroeconomics: Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
- R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-03-14 (All new papers)
- NEP-CMP-2009-03-14 (Computational Economics)
- NEP-FOR-2009-03-14 (Forecasting)
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.:
- Suahasil Nazara & Geoffrey J.D. Hewings, 2004. "Spatial Structure and Taxonomy of Decomposition in Shift-Share Analysis," Growth and Change, Gatton College of Business and Economics, University of Kentucky, vol. 35(4), pages 476-490.
- 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 and Francis Journals, vol. 1(1), pages 7-30.
- 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.
- Roberto Patuelli & Daniel A. Griffith & Michael Tiefelsdorf & Peter Nijkamp, 2009.
"Spatial Filtering and Eigenvector Stability: Space-Time Models for German Unemployment Data,"
Quaderni della facoltÃ di Scienze economiche dell'UniversitÃ 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, , 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 & 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.
- Roberto Patuelli & Simonetta Longhi & Aura Reggiani & Peter Nijkamp, 2005. "Forecasting Regional Employment in Germany by Means of Neural Networks and Genetic Algorithms," Computational Economics 0511002, EconWPA.
- 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.
- 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).
- 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..
- Gromicho, J.A.S. & Hoorn, J.J. van & Timmer, G.T. & Saldanha-da-Gama, F., 2009. "Exponentially better than brute force: solving the jobshop scheduling problem optimally by dynamic programming," Serie Research Memoranda 0056, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Alessio Tutino).
If references are entirely missing, you can add them using this form.