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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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    2. Xiyun Wang & Xianglong Tang & Jin Shi & Pengzhen Du, 2024. "Construction and Optimization of Urban and Rural Ecological Security Patterns Based on Ecological Service Importance in a Semi-Arid Region: A Case Study of Lanzhou City," Sustainability, MDPI, vol. 16(14), pages 1-22, July.
    3. 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.
    4. 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.
    5. 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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    Keywords

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    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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