Application of Feed-Forward Neural Networks Autoregressive Models in Gross Domestic Product Prediction

Authors: Ε. Giovanis

Abstract:

In this paper we present an autoregressive model with neural networks modeling and standard error backpropagation algorithm training optimization in order to predict the gross domestic product (GDP) growth rate of four countries. Specifically we propose a kind of weighted regression, which can be used for econometric purposes, where the initial inputs are multiplied by the neural networks final optimum weights from input-hidden layer after the training process. The forecasts are compared with those of the ordinary autoregressive model and we conclude that the proposed regression-s forecasting results outperform significant those of autoregressive model in the out-of-sample period. The idea behind this approach is to propose a parametric regression with weighted variables in order to test for the statistical significance and the magnitude of the estimated autoregressive coefficients and simultaneously to estimate the forecasts.

Keywords: Autoregressive model, Error back-propagation Feed-Forward neural networks, , Gross Domestic Product

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1334377

References:

[1] R.D. Aryal and W. Yao-Wu, "Neural Network Forecasting of the Production Level of Chinese Construction Industry", Journal of Comparative International Management , vol. 29, pp. 319-33, 2003
[2] N.R Swanson and H. White, "A model selection approach to real time macroeconomic forecasting using linear models and artificial neural networks", Review of Economics and Statistics, vol. 79, pp. 540-650, 1997a
[3] N.R Swanson and H. White, "Forecasting economic time series using adaptive versus non-adaptive and linear versus nonlinear econometric models", International Journal of Forecasting, vol. 13, pp. 439-461, 1997b
[4] A. Keles, M. Kolcak and A. Keles, "The adaptive neuro-fuzzy model for forecasting the domestic debt", Knowledge-Based Systems , vol. 21, no. 8, pp. 951-957, 2008
[5] W.H Greene, Econometric Analysis, Sixth Edition, Prentice Hall, New Jersey, 2008, pp. 560-579
[6] Haykin, S. (1999), NEURAL NETWORKS: A Comprehensive Foundation, Second Edition Pearson education, Prentice Hall, Delhi, India, 1999, pp. 33-47, 73-76
[7] Graupe, D. Principles of Artificial Neural Networks, Second Edition, Advanced Series on Circuits and Systems, 6, World Scientific Co., Singapore, 2007, pp. 10-15, 20-24, 59-63
[8] B. Widrow, and M. Hoff, E., "Adaptive switching circuits," In Western Electronic Show and Convention Record, Institute of Radio Engineers (now IEEE), vol. 4, pp. 96-104, 1960
[9] D. A. Dickey, and W. A. Fuller, "Distribution of the Estimators for Autoregressive Time Series with a Unit Root", Journal of the American Statistical Association, vol. 74, pp. 427-431, 1979
[10] J. G. MacKinnon, "Numerical Distribution Functions for Unit Root and Cointegration Tests", Journal of Applied Econometrics, vol. 11, pp. 601- 618, 1996