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Evaluating Neural Spatial Interaction. Modelling By Bootstrapping

Author

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  • Fischer, Manfred M.
  • Reismann, Martin
  • Hlavackova-Schindler, Katerina

Abstract

This paper exposes problems of the commonly used technique of splitting the available data in neural spatial interaction modelling into training, validation, and test sets that are held fixed and warns about drawing too strong conclusions from such static splits. Using a bootstrapping procedure, we compare the uncertainty in the solution stemming from the data splitting with model specific uncertainties such as parameter initialization. Utilizing the Austrian interregional telecommunication traffic data and the differential evolution method for solving the parameter estimation task for a fixed topology of the network model [ i.e. J = 9] this paper illustrates that the variation due to different resamplings is significantly larger than the variation due to different parameter initializations. This result implies that it is important to not over-interpret a model, estimated on one specific static split of the data.
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Suggested Citation

  • Fischer, Manfred M. & Reismann, Martin & Hlavackova-Schindler, Katerina, 2000. "Evaluating Neural Spatial Interaction. Modelling By Bootstrapping," ERSA conference papers ersa00p370, European Regional Science Association.
    • Handle: RePEc:wiw:wiwrsa:ersa00p370
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    Cited by:

    1. Fischer, Manfred M. & Reismann, Martin, 2002. "A methodology for neural spatial interaction modelling," ERSA conference papers ersa02p034, European Regional Science Association.
    2. Giuseppe Bruno & Andrea Genovese, 2012. "A Spatial Interaction Model for the Representation of the Mobility of University Students on the Italian Territory," Networks and Spatial Economics, Springer, vol. 12(1), pages 41-57, March.
    3. Manfred M. Fischer, 2009. "Principles of Neural Spatial Interaction Modeling," Advances in Spatial Science, in: Michael Sonis & Geoffrey J. D. Hewings (ed.), Tool Kits in Regional Science, chapter 8, pages 199-214, Springer.
    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.

    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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