Modeling and forecasting economic growth by analyzing nonlinear time series

Document Type: Research Paper

Authors

1 Master student industrial Engineering ,University of sistan and Baluchestan

2 null

3 Assistant Professor, industrial Management, University of Imam khomeini

Abstract

Correct prediction of economic growth in the long-term policy planning and sustainable development, plays an important role. One of the major issues in the forecast time series is methods to identify patterns to control the complexity and Optimization of forecasting error. In this study, non-linear time series analysis GDP to forecast the economic growth path using , bayesian neural networks, for greater flexibility of the Nonlinear model in dealing with the complexities and more compatible with the actual conditions. Then, we use combination of genetic meta-heuristic algorithms to improve efficiency in network training model results and it is compared to older discussed methods. Model used 1992 to 1992 datas to estimation and tested 2014 to first two seasons of 2016 datas using the SSE and MSE. Results show that the complexity of the reform in the education network will have an important role in the deduction of optimization error.

Keywords

Main Subjects


  1. Granger, C. W. J., and Newbold, P. (2014). Forecasting Economic Time Series: Academic Press.
  2. Cutright, P. (1965). Political structure, economic development, and national social security programs. American journal of sociology, Vol.70, No.5, PP. 537-550.
  3. Bjork, G. J. (1999). The Way it Worked and Why It Won’t: Structural Change and the Slowdown of U.S. Economic Growth: Praeger; Complete Numbers Starting.
  4. Jones, C. L. (2013). Introduction to Economic Growth (3 Edition Ed.): W. W. Norton and Compan;.
  5. Einicke, G. A. (2012). Smoothing, Filtering and Prediction: Estimating the Past, Present and Future: Intech.
  6. Stock, J. H., and Watson, M. W. (2003). Introduction to Econometrics , Addison Wesley Boston.
  7. Baffigi, A., Golinelli, R., and Parigi, G. (2004). “Bridge Models to Forecast the Euro Area GDP”, International Journal of Forecasting, Vol. 20, No. 3, PP. 447-460.
  8. Schumacher, C. (2007). “Forecasting German GDP Using Alternative Factor Models Based on Large Datasets”, Journal of Forecasting, Vol. 26, No. 4, PP. 271-302.
  9. Schumacher, C., and Breitung, J. (2008). “Real-Time Forecasting of German GDP Based on a Large Factor Model with Monthly and Quarterly Data”, International Journal of Forecasting, Vol. 24, No. 3, PP. 386-398.
10. Marcellino, M., and Schumacher, C. (2010). “Factor MIDAS for Nowcasting and Forecasting with Ragged‐Edge Data: A Model Comparison for German GDP”, Oxford Bulletin of Economics and Statistics, Vol. 72, No. 4, PP. 518-550.

11. Bańbura, M., and Rünstler, G. (2011). “A Look Into the Factor Model Black Box: Publication Lags and the Role of Hard and Soft Data in Forecasting GDP”, International Journal of Forecasting, Vol. 27, No. 2, PP. 333-346.

12. Zhang, G. P., and Qi, M. (2005). “Neural Network Forecasting for Seasonal and Trend Time Series”, European Journal of Operational Research, Vol. 160, No. 2, PP. 501-514.

13. Pouzols, F. M., Lendasse, A., and Barros, A. B. (2010). “Autoregressive Time Series Prediction by Means of Fuzzy Inference Systems Using Nonparametric Residual Variance Estimation”, Fuzzy Sets and Systems, Vol. 161, No. 4, PP. 471-497.

14. Giovanis, Ε. (2010), A Study of Panel Logit Model and Adaptive Neuro-Fuzzy Inference System in the Prediction of Financial Distress Periods, World Academy of Science, Engineering and Technology, Vol. 64, PP. 646-652.

15. Tealab, A. (2018). Time series forecasting using artificial neural networks methodologies: A systematic review, Future Computing and Informatics Journal, Vol. 3, No. 2, PP. 334 – 340.

16. Box, G. E. P., Jenkins, G. M., Reinsel, G. C., and Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control: John Wiley and Sons.

17. Davidson, R., and Mackinnon, J. G. (1993). Estimation and Inference In Econometrics.

18. Fuller, W. A. (2009). Introduction to Statistical Time Series: John Wiley and Sons.

19. Geman, S., Bienenstock, E., and Doursat, R. (1992). “Neural Networks and the Bias/Variance Dilemma”, Neural Computation, Vol. 4, No. 1, PP. 1-58.

20. Liang, F. (2005). “Bayesian Neural Networks for Nonlinear Time Series Forecasting”, Statistics and Computing, Vol. 15, No. 1, PP. 13-29.

21. Mackay, D. J. (1995). “Probable Networks and Plausible Predictions—A Review of Practical Bayesian Methods for Supervised Neural Networks”, Network: Computation in Neural Systems, Vol. 6, No. 3, PP. 469-505.

22. Neal, R. M. (1996). “Priors for Infinite Networks”, Bayesian Learning for Neural Networks (PP. 29-53): Springer.

23. Bishop, C. M. (1995). Neural Networks for Pattern Recognition: Oxford University Press.

24. Holmes, C., and Mallick, B. (1998). “Bayesian Radial Basis Functions of Variable Dimension”, Neural Computation, Vol. 10, No. 5, PP. 1217-1233.

25. Freitas, J. (2000). Bayesian Methods for Neural Networks [Ph. D. Thesis]: Trinity College University of Cambridge and Cambridge University Engineering Department, Cambridge, UK.

26. Mackay, D. J. (1992). “A Practical Bayesian Framework for Backpropagation Networks”, Neural Computation, Vol. 4, No. 3, PP. 448-472.

27. Chua, C., and Goh, A. (2003). “Nonlinear Modeling with Confidence Estimation Using Bayesian Neural Networks”, International Journal for Numerical and Analytical Methods in Geomechanics, No. 27, PP. 651-667.

28. Kocadağlı, O., and Aşıkgil, B. (2014). “Nonlinear Time Series Forecasting with Bayesian Neural Networks”, Expert Systems with Applications, Vol. 41, No. 15, PP. 6596-6610.

29. Chambers JA, Sherliker W and Mandic DP 2000 A normalised gradient algorithm for an adaptive recurrent perceptron. In Proc. Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP-2000), Vol. 1, PP. 396–399.

30. Marwala, T. (2007). Bayesian training of neural networks using genetic programming. Pattern Recognition Letters, Vol. 28, No. 12, PP. 1452-1458.

31. Michalewicz, Z., : Genetic algorithms + data STRUCTURES = evolution programs. Springer, New York (1996)