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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.

Suggested Citation

  • Moshiri, Saeed & Cameron, Norman E & Scuse, David, 1999. "Static, Dynamic, and Hybrid Neural Networks in Forecasting Inflation," Computational Economics, Springer;Society for Computational Economics, vol. 14(3), pages 219-235, December.
  • Handle: RePEc:kap:compec:v:14:y:1999:i:3:p:219-35
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    References listed on IDEAS

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    1. Kendrick, David A & Amman, Hans M, 1999. "Programming Languages in Economics," Computational Economics, Springer;Society for Computational Economics, vol. 14(1-2), pages 151-181, October.
    2. Siem Jan Koopman & Neil Shephard & Jurgen A. Doornik, 1999. "Statistical algorithms for models in state space using SsfPack 2.2," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 107-160.
    3. Podivinsky, Jan M, 1999. " Ox 2.10: Beast of Burden or Object of Desire?," Journal of Economic Surveys, Wiley Blackwell, vol. 13(4), pages 491-502, September.
    4. Marius Ooms, 1999. "Review of SsfPack 2.2: statistical algorithms for models in state space," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 161-166.
    5. MacKinnon, James G, 1999. "The Linux Operating System: Debian GNU/Linux," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(4), pages 443-452, July-Aug..
    6. Cribari-Neto, Francisco, 1997. "Econometric Programming Environments: GAUSS, Ox and S-PLUS," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(1), pages 77-89, Jan.-Feb..
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    Cited by:

    1. Yochanan Shachmurove & Doris Witkowska, "undated". "Utilizing Artificial Neural Network Model to Predict Stock Markets," Penn CARESS Working Papers cae679cdc2e020f74d692ae73, Penn Economics Department.
    2. Christian A. Johnson & Rodrigo Vergara, 2005. "The implementation of monetary policy in an emerging economy: the case of Chile," Revista de Analisis Economico – Economic Analysis Review, Ilades-Georgetown University, Universidad Alberto Hurtado/School of Economics and Bussines, vol. 20(1), pages 45-62, June.
    3. Hong, Seung Hyun & Phillips, Peter C. B., 2010. "Testing Linearity in Cointegrating Relations With an Application to Purchasing Power Parity," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 96-114.
    4. Charalampos Stasinakis & Georgios Sermpinis & Konstantinos Theofilatos & Andreas Karathanasopoulos, 2016. "Forecasting US Unemployment with Radial Basis Neural Networks, Kalman Filters and Support Vector Regressions," Computational Economics, Springer;Society for Computational Economics, vol. 47(4), pages 569-587, April.
    5. M. Ali Choudhary & Adnan Haider, 2012. "Neural network models for inflation forecasting: an appraisal," Applied Economics, Taylor & Francis Journals, vol. 44(20), pages 2631-2635, July.
    6. Karol Szafranek, 2017. "Bagged artificial neural networks in forecasting inflation: An extensive comparison with current modelling frameworks," NBP Working Papers 262, Narodowy Bank Polski, Economic Research Department.
    7. 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.
    8. Laura Brown & Saeed Moshiri, 2004. "Unemployment variation over the business cycles: a comparison of forecasting models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(7), pages 497-511.

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