Application of Feed-Forward Neural Networks Autoregressive Models in Gross Domestic Product Prediction
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.
[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
[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
@article{"International Journal of Business, Human and Social Sciences:56842", author = "Ε. Giovanis", title = "Application of Feed-Forward Neural Networks Autoregressive Models in Gross Domestic Product Prediction", 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", volume = "4", number = "4", pages = "388-5", }