Static, Dynamic, and Hybrid Neural Networks in Forecasting Inflation
Computational Economics, Forthcoming
17 Pages Posted: 25 Aug 1998 Last revised: 4 Oct 2013
Date Written: November 24, 1998
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, i.e., radial basis function network (RBFN) model and recurrent neural network (RNN) model, in the context of forecasting inflation. The RBFN model is a hybrid model whose learning process is much faster than the BPN model and 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 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.
JEL Classification: C22, C45, E31, E37
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