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Static, Dynamic, and Hybrid Neural Networks in Forecasting Inflation

Listed author(s):
  • Moshiri, Saeed
  • Cameron, Norman E
  • Scuse, David

The back-propagation neural network (BPN) model has been the most popular form of artificial neural network model used for forecasting, particularly in economics and finance. It is a static (feed-forward) model which has a learning process in both hidden and output layers. In this paper we compare the performance of the BPN model with that of two other neural network models, viz., the radial basis function network (RBFN) model and the recurrent neural network (RNN) model, in the context of forecasting inflation. The RBFN model is a hybrid model with a learning process that is much faster than the BPN model and that is able to generate almost the same results as the BPN model. The RNN model is a dynamic model which allows feedback from other layers to the input layer, enabling it to capture the dynamic behavior of the series. The results of the ANN models are also compared with those of the econometric time series models. Citation Copyright 1999 by Kluwer Academic Publishers.

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Article provided by Springer & Society for Computational Economics in its journal Computational Economics.

Volume (Year): 14 (1999)
Issue (Month): 3 (December)
Pages: 219-235

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Handle: RePEc:kap:compec:v:14:y:1999:i:3:p:219-35
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