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A Rank-Order Test on the Statistical Performance of Neural Network Models for Regional Labor Market Forecasts

  • Patuelli, Roberto

    (Free U Amsterdam)

  • Longhi, Simonetta

    (ISER, U Essex)

  • Reggiani, Aura

    (U Bologna)

  • Nijkamp, Peter

    (Free U Amsterdam)

  • Blien, Uwe

    (Institut fur Arbeitsmarkt und Berufsforschung, Nuremburg)

Using a panel of 439 German regions, we evaluate and compare the performance of various Neural Network (NN) models as forecasting tools for regional employment growth. Because of relevant differences in data availability between the former East and West Germany, the NN models are computed separately for the two parts of the country. The comparisons of the models and their ex post forecasts are carried out by means of a non-parametric test: viz. the Friedman statistic. The Friedman statistic tests the consistency of model results obtained in terms of their rank order. Since there is no normal distribution assumption, this methodology is an interesting substitute for a standard analysis of variance.

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Article provided by Southern Regional Science Association in its journal Review of Regional Studies.

Volume (Year): 37 (2007)
Issue (Month): 1 ()
Pages: 64-81

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Handle: RePEc:rre:publsh:v:37:y:2007:i:1:p:64-81
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  1. Simonetta Longhi & Peter Nijkamp & Aura Reggianni & Erich Maierhofer, 2005. "Neural Network Modeling as a Tool for Forecasting Regional Employment Patterns," International Regional Science Review, SAGE Publishing, vol. 28(3), pages 330-346, July.
  2. Profit, Stefan & Tschernig, Rolf, 1998. "Germany's labor market problems: What to do and what not to do? A survey among experts," SFB 373 Discussion Papers 1998,94, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  3. Antonino Scarelli & Lorenzo Venzi, 1997. "Nonparametric Statistics In Multicriteria Analysis," Theory and Decision, Springer, vol. 43(1), pages 89-105, July.
  4. Frees, Edward W., 1995. "Assessing cross-sectional correlation in panel data," Journal of Econometrics, Elsevier, vol. 69(2), pages 393-414, October.
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