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

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Author Info

  • 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.

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File URL: http://128.118.178.162/eps/exp/papers/0511/0511001.pdf
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Bibliographic Info

Paper provided by EconWPA in its series Experimental with number 0511001.

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Length: 26 pages
Date of creation: 08 Nov 2005
Date of revision:
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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Web page: http://128.118.178.162

Related research

Keywords: multicriteria analysis; neural networks; regional labour markets;

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  1. Xavier Sala-i-Martin, 1997. "I just ran four million regressions," Economics Working Papers 201, Department of Economics and Business, Universitat Pompeu Fabra.
  2. 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.
  3. Edwin Hinloopen & Peter Nijkamp, 1990. "Qualitative multiple criteria choice analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 24(1), pages 37-56, February.
  4. 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.
  5. Uwe Blien & Alexandros Tassinopoulos, 2001. "Forecasting Regional Employment with the ENTROP Method," Regional Studies, Taylor & Francis Journals, vol. 35(2), pages 113-124.
  6. 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.
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