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Modelling Hypothetical Wage Equation by Neural Networks

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Artificial Neural Networks and Machine Learning – ICANN 2011 (ICANN 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6792))

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Abstract

In this paper, a hypothetical wage equation is modelled using quarterly data from United Kingdom. Wage and price data have a great importance for the overall features of large-scale macro models and for example for the different policy actions. The modelled feature in this paper is the real wage, the differential of a nominal wage and a price index. In the variable selection phase, the stationary properties of the data were investigated by augmented Dickey-Fuller tests (ADF). The main idea in this paper is to present a neural network model, which has a better fit than conventional MLR model.

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Author information

Authors and Affiliations

  1. Aalto University School of Science, P.O. Box 15400, FI-00076, Aalto, Finland

    Jaakko Talonen & Miki Sirola

Authors
  1. Jaakko Talonen
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  2. Miki Sirola
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Editor information

Editors and Affiliations

  1. Department of Information and Computer Science, Aalto University School of Science, P.O. Box 15400, 00076, Aalto, Finland

    Timo Honkela  & Samuel Kaski  & 

  2. School of Physics, Astronomy and Informatics, Department of Informatics, Nicolaus Copernicus University, ul. Grudziadzka 5, 87-100, Torun, Poland

    Włodzisław Duch

  3. Department of Statistical Science, University College London, 1-19 Torrington Place, WC1E 7HB, London, UK

    Mark Girolami

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© 2011 Springer-Verlag Berlin Heidelberg

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Talonen, J., Sirola, M. (2011). Modelling Hypothetical Wage Equation by Neural Networks. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2011. ICANN 2011. Lecture Notes in Computer Science, vol 6792. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21738-8_49

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  • DOI: https://doi.org/10.1007/978-3-642-21738-8_49

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21737-1

  • Online ISBN: 978-3-642-21738-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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