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Neural networks for cross-sectional employment forecasts: a comparison of model specifications for germany

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  • Patuelli, R.

    (Vrije Universiteit Amsterdam, Faculteit der Economische Wetenschappen en Econometrie (Free University Amsterdam, Faculty of Economics Sciences, Business Administration and Economitrics)

  • Reggiani, A.
  • Nijkamp, P.

Abstract

In 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 distinct 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.

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Bibliographic Info

Paper provided by VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics in its series Serie Research Memoranda with number 0014.

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Date of creation: 2009
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Handle: RePEc:dgr:vuarem:2009-14

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Web page: http://www.feweb.vu.nl

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  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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).
  6. 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.
  7. 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.
  8. 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..
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Cited by:
  1. Gromicho, J.A.S. & Hoorn, J.J. van & Timmer, G.T., 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.

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