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Multicriteria Analysis of Neural Network Forecasting Models: An Application to German Regional Labour Markets

Author

Listed:
  • Roberto Patuelli

    (Vrije Universiteit)

  • Simonetta Longhi

    (University of Essex)

  • Aura Reggiani

    (University of Bologna)

  • Peter Nijkamp

    (Vrije Universiteit)

Abstract

This paper develops a flexible multi-dimensional assessment method for the comparison of different statistical-econometric techniques based on learning mechanisms, with a view to analysing and forecasting regional labour markets. The aim of this paper is twofold. A first major objective is to explore the use of a standard choice tool, namely Multicriteria Analysis (MCA), in order to cope with the intrinsic methodological uncertainty on the choice of a suitable statistical- econometric learning technique for regional labour market analysis. MCA is applied here to support choices on the performance of various models – based on classes of Neural Network (NN) techniques – that serve to generate employment forecasts in West Germany at a regional/district level. A second objective of the paper is to analyse the methodological potential of a blend of approaches (NN-MCA) in order to extend the analysis framework to other economic research domains, where formal models are not available, but where a variety of statistical data is present. The paper offers a basis for a more balanced judgement of the performance of rival statistical tests.

Suggested Citation

  • Roberto Patuelli & Simonetta Longhi & Aura Reggiani & Peter Nijkamp, 2005. "Multicriteria Analysis of Neural Network Forecasting Models: An Application to German Regional Labour Markets," Experimental 0511001, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpex:0511001
    Note: Type of Document - pdf; pages: 26. Published in: Studies in Regional Science 33 (3): 205-230
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    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/exp/papers/0511/0511001.pdf
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    References listed on IDEAS

    as
    1. Edwin Hinloopen & Peter Nijkamp, 1990. "Qualitative multiple criteria choice analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 24(1), pages 37-56, February.
    2. Sala-i-Martin, Xavier, 1997. "I Just Ran Two Million Regressions," American Economic Review, American Economic Association, vol. 87(2), pages 178-183, May.
    3. Uwe Blien & Alexandros Tassinopoulos, 2001. "Forecasting Regional Employment with the ENTROP Method," Regional Studies, Taylor & Francis Journals, vol. 35(2), pages 113-124.
    4. Longhi, Simonetta & Nijkamp, Peter & Reggiani, Aura & Blien, Uwe, 2002. "Forecasting regional labour markets in Germany: an evaluation of the performance of neural network analysis," ERSA conference papers ersa02p117, European Regional Science Association.
    5. Reggiani, Aura & Nijkamp, Peter & Sabella, Enrico, 2001. "New advances in spatial network modelling: Towards evolutionary algorithms," European Journal of Operational Research, Elsevier, vol. 128(2), pages 385-401, January.
    6. Manfred M. Fischer & Yee Leung, 1998. "A genetic-algorithms based evolutionary computational neural network for modelling spatial interaction data," ERSA conference papers ersa98p478, European Regional Science Association.
    7. Aura Reggiani (ed.), 2000. "Spatial Economic Science," Advances in Spatial Science, Springer, number 978-3-642-59787-9, February.
    Full references (including those not matched with items on IDEAS)

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    JEL classification:

    • C9 - Mathematical and Quantitative Methods - - Design of Experiments

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