Static, Dynamic, and Hybrid Neural Networks in Forecasting Inflation

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.

[1]  B. Kermanshahi,et al.  Feedforward versus recurrent neural networks for forecasting monthly japanese yen exchange rates , 1996 .

[2]  Saeed Moshiri,et al.  Neural Network versus Econometric Models in Forecasting Inflation , 1999 .

[3]  Howard B. Demuth,et al.  Neutral network toolbox for use with Matlab , 1995 .

[4]  Michael I. Jordan.  Serial Order: A Parallel Distributed Processing Approach , 1997 .

[5]  William Remus,et al.  Neural Network Models for Time Series Forecasts , 1996 .

[6]  Chung-Ming Kuan,et al.  Forecasting exchange rates using feedforward and recurrent neural networks , 1992 .

[7]  James D. Keeler,et al.  Layered Neural Networks with Gaussian Hidden Units as Universal Approximations , 1990, Neural Computation.

[8]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[9]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[10]  Efraim Turban,et al.  Neural Networks in Finance and Investing: Using Artificial Intelligence to Improve Real-World Performance , 1992 .

[11]  Esfandiar Maasoumi,et al.  Artificial neural networks for some macroeconomic series: A first report , 1994 .

[12]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[13]  Kurt Hornik,et al.  A Convergence Result for Learning in Recurrent Neural Networks , 1994, Neural Computation.

[14]  Philip D. Wasserman,et al.  Advanced methods in neural computing , 1993, VNR computer library.

[15]  Halbert White,et al.  Artificial neural networks: an econometric perspective ∗ , 1994 .

[16]  Norman R. Swanson,et al.  A Model Selection Approach to Real-Time Macroeconomic Forecasting Using Linear Models and Artificial Neural Networks , 1997, Review of Economics and Statistics.