Neural networks in economic modelling: An empirical study

W.J.H. Verkooijen

    Research output: ThesisDoctoral Thesis

    747 Downloads (Pure)


    This dissertation addresses the statistical aspects of neural networks and their usability for solving problems in economics and finance. Neural networks are discussed in a framework of modelling which is generally accepted in econometrics. Within this framework a neural network is regarded as a statistical technique that implements a model-free regression strategy. Model-free regression seems particularly useful in situations where economic theory cannot provide sensible model specifications. Neural networks are applied in three case studies: modelling house prices; predicting the production of new mortgage loans; predicting the foreign exchange rates. From these case studies is concluded that neural networks are a valuable addition to the econometrician's toolbox, but that they are no panacea.
    Original languageEnglish
    QualificationDoctor of Philosophy
    Awarding Institution
    • Tilburg University
    • Daniels, Hennie, Promotor
    • Plasmans, J.E.J., Promotor
    Award date9 Feb 1996
    Place of PublicationTilburg
    Print ISBNs9056680102
    Publication statusPublished - 1996


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