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Static, Dynamic, and Hybrid Neural Networks in Forecasting Inflation

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  • Moshiri, Saeed
  • Cameron, Norman E
  • Scuse, David

Abstract

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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Bibliographic Info

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

Volume (Year): 14 (1999)
Issue (Month): 3 (December)
Pages: 219-35

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

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Cited by:
  1. Seung Hyun Hong & Peter C. B. Phillips, 2005. "Testing Linearity in Cointegrating Relations with an Application to Purchasing Power Parity," Cowles Foundation Discussion Papers 1541, Cowles Foundation for Research in Economics, Yale University.
  2. Christian A Johnson & Rodrigo Vergara, 2005. "The Implementation of Monetary Policy in an Emerging Economy: The Case of Chile," Documentos de Trabajo 291, Instituto de Economia. Pontificia Universidad Católica de Chile..
  3. Binner, J.M. & Tino, P. & Tepper, J. & Anderson, R. & Jones, B. & Kendall, G., 2010. "Does money matter in inflation forecasting?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4793-4808.
  4. Yochanan Shachmurove & Doris Witkowska, . "Utilizing Artificial Neural Network Model to Predict Stock Markets," Penn CARESS Working Papers cae679cdc2e020f74d692ae73, Penn Economics Department.

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