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Neural Network Modeling as a Tool for Forecasting Regional Employment Patterns

Author

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  • Simonetta Longhi

    (Department of Spatial Economics, Free University, Amsterdam, the Netherlandsslonghi@feweb.vu.nl)

  • Peter Nijkamp

    (Department of Spatial Economics, Free University, Amsterdam, the Netherlandspnijkamp@feweb.vu.nl)

  • Aura Reggianni

    (Department of Economics, Faculty of Statistics, University of Bologna, Bologna, Italyreggiani@economia.unibo.it)

  • Erich Maierhofer

    (Institut fuer Arbeitsmarkt und Berufsforschung (IAB), Nuremberg, Germanyerich.maierhofer@iab.de)

Abstract

This article analyzes artificial neural networks (ANNs) as a method to compute employment forecasts at a regional level. The empirical application is based on employment data collected for 327West German regionsover a periodof fourteenyears. First, the authors compare ANNs to models commonly used in panel data analysis. Second, they verify, in the case of panel data, whether the common practice of combining forecasts of the computed models is able to produce more reliable forecasts. The technique currently employed by the German authorities to compute such regional employment forecasts is comparable to a simple naïve no-change model. For this reason, ANNs are also compared to this undemanding technique.

Suggested Citation

  • Simonetta Longhi & Peter Nijkamp & Aura Reggianni & Erich Maierhofer, 2005. "Neural Network Modeling as a Tool for Forecasting Regional Employment Patterns," International Regional Science Review, , vol. 28(3), pages 330-346, July.
    • Handle: RePEc:sae:inrsre:v:28:y:2005:i:3:p:330-346
    DOI: 10.1177/0160017605276187
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    Cited by:

    1. Maria Francesca Cracolici & Miranda Cuffaro & Peter Nijkamp, 2007. "Geographical Distribution of Unemployment: An Analysis of Provincial Differences in Italy," Growth and Change, Wiley Blackwell, vol. 38(4), pages 649-670, December.
    2. Valerij Gamukin, 2017. "Structural Change of Gross Regional Product in the Subjects of Ural Federal District," Economy of region, Centre for Economic Security, Institute of Economics of Ural Branch of Russian Academy of Sciences, vol. 1(2), pages 410-421.
    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. Messner, Wolfgang, 2024. "Distance is the spice, but not the whole enchilada: Country-pair psychic distance stimuli and country fixed effects in a deep learning implementation of the trade flow model," International Business Review, Elsevier, vol. 33(1).
    5. Longhi, Simonetta & Nijkamp, Peter, 2006. "Forecasting regional labor market developments under spatial heterogeneity and spatial correlation," Serie Research Memoranda 0015, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    6. V. Gamukin V. & В. Гамукин В., 2018. "Управление структурой валового регионального продукта в субъектах Южного федерального округа // Managing the Gross Regional Product Structure in the Territorial Subjects of the Southern Federal Distri," Управленческие науки // Management Science, ФГОБУВО Финансовый университет при Правительстве Российской Федерации // Financial University under The Government of Russian Federation, vol. 8(2), pages 18-29.
    7. Simonetta Longhi & Peter Nijkamp, 2007. "Forecasting Regional Labor Market Developments under Spatial Autocorrelation," International Regional Science Review, , vol. 30(2), pages 100-119, April.
    8. repec:rre:publsh:v:37:y:2007:i:1:p:64-81 is not listed on IDEAS
    9. 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).
    10. de Lucio, Juan, 2021. "Estimación adelantada del crecimiento regional mediante redes neuronales LSTM," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 49, pages 45-64.

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