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Neural Networks for Regional Employment Forecasts: Are the Parameters Relevant?

Author

Listed:
  • Roberto Patuelli

    (University of Lugano, Switzerland and The Rimini Centre for Economic Analysis, Italy)

  • Aura Reggiani

    (University of Bologna, Italy)

  • Peter Nijkamp

    (VU University Amsterdam, The Netherlands)

  • Norbert Schanne

    (Institute for Employment Research (IAB), Nuremberg, Germany)

Abstract

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.

Suggested Citation

  • Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Norbert Schanne, 2009. "Neural Networks for Regional Employment Forecasts: Are the Parameters Relevant?," Working Paper series 07_09, Rimini Centre for Economic Analysis, revised Feb 2010.
    • Handle: RePEc:rim:rimwps:07_09
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    References listed on IDEAS

    as
    1. Roberto Patuelli & Peter Nijkamp & Simonetta Longhi & Aura Reggiani, 2008. "Neural Networks and Genetic Algorithms as Forecasting Tools: A Case Study on German Regions," Environment and Planning B, , vol. 35(4), pages 701-722, August.
    2. 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, September.
    3. 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).
    4. Roberto Patuelli & Daniel A. Griffith & Michael Tiefelsdorf & Peter Nijkamp, 2006. "The Use of Spatial Filtering Techniques: The Spatial and Space-time Structure of German Unemployment Data," Tinbergen Institute Discussion Papers 06-049/3, Tinbergen Institute.
    5. 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.
    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. M M Fischer, 1998. "Computational Neural Networks: A New Paradigm for Spatial Analysis," Environment and Planning A, , vol. 30(10), pages 1873-1891, October.
    8. 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.
    9. Roberto Patuelli & Peter Nijkamp & Simonetta Longhi & Aura Reggiani, 2008. "Neural Networks and Genetic Algorithms as Forecasting Tools: A Case Study on German Regions," Environment and Planning B, , vol. 35(4), pages 701-722, August.
    10. 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.
    11. 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 347-364, Oct.-Dec..
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    13. Zaiyong Tang & Paul A. Fishwick, 1993. "Feedforward Neural Nets as Models for Time Series Forecasting," INFORMS Journal on Computing, INFORMS, vol. 5(4), pages 374-385, November.
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    Cited by:

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    3. Esteban Fernández-Vázquez & Blanca Moreno, 2017. "Entropy Econometrics for combining regional economic forecasts: A Data-Weighted Prior Estimator," Journal of Geographical Systems, Springer, vol. 19(4), pages 349-370, October.
    4. Zhou, You & Zhang, Lingzhu & Chiaradia, Alain J F, 2021. "An adaptation of reference class forecasting for the assessment of large-scale urban planning vision, a SEM-ANN approach to the case of Hong Kong Lantau tomorrow," Land Use Policy, Elsevier, vol. 109(C).

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    More about this item

    Keywords

    neural networks; sensitivity analysis; employment forecasts; local labour markets;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • E27 - Macroeconomics and Monetary Economics - - 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

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