Modelling hypothetical wage equation by neural networks

Authors:
Jaakko Talonen;Miki Sirola
Affiliations:
Aalto University School of Science, Aalto, Finland;Aalto University School of Science, Aalto, Finland
Venue:
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Year:
2011

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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.