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Forecasting Regional Employment in Germany by Means of Neural Networks and Genetic Algorithms

Author

Listed:
  • Roberto Patuelli

    (Vrije Universiteit)

  • Simonetta Longhi

    (University of Essex)

  • Aura Reggiani

    (University of Bologna)

  • Peter Nijkamp

    (Vrije Universiteit)

Abstract

The aim of this paper is to develop and apply Neural Network (NN) models in order to forecast regional employment patterns in Germany. NNs are statistical tools based on learning algorithms with a distribution over a large amount of quantitative data. NNs are increasingly deployed in the social sciences as a useful technique for interpolating data when a clear specification of the functional relationship between dependent and independent variables is not available. In addition to traditional NN models, a further set of NN models will be developed in this paper, incorporating Genetic Algorithm (GA) techniques in order to detect the networks’ structure. GAs are computer-aided optimization tools that imitate natural biological evolution in order to find the solution that best fits the given case. Our experiments employ a data set consisting of a panel of 439 districts distributed over the former West and East Germany,. The West and East data sets have different time horizons, as employment information by district is available from 1987 and 1993 for West and East Germany, respectively. Separate West and East models are tested, before carrying out a unified experiment on the full data set for Germany. The above models are then evaluated by means of several statistical indicators, in order to test their ability to provide out- of-sample forecasts. A comparison between traditional and GAenhanced models is ultimately proposed. The results show that the West and East NN models perform with different degrees of precision, because of the different data sets’ time horizons.

Suggested Citation

  • 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, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpco:0511002
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    References listed on IDEAS

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    Cited by:

    1. Wozniak Marcin, 2020. "Forecasting the unemployment rate over districts with the use of distinct methods," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(2), pages 1-20, April.
    2. 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.
    3. Robert Lehmann & Klaus Wohlrabe, 2014. "Regional economic forecasting: state-of-the-art methodology and future challenges," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 218-231.
    4. Matías Mayor & Roberto Patuelli, 2012. "Short-Run Regional Forecasts: Spatial Models through Varying Cross-Sectional and Temporal Dimensions," Advances in Spatial Science, in: Esteban Fernández Vázquez & Fernando Rubiera Morollón (ed.), Defining the Spatial Scale in Modern Regional Analysis, edition 127, chapter 0, pages 173-192, Springer.
    5. Ferenc Bakó & Judit Berkes & Cecília Szigeti, 2021. "Households’ Electricity Consumption in Hungarian Urban Areas," Energies, MDPI, vol. 14(10), pages 1-23, May.
    6. 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

    forecasting; neural networks; regional labour markets;
    All these keywords.

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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