Application of Adaptive Network-Based Fuzzy Inference System in Macroeconomic Variables Forecasting
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Application of Adaptive Network-Based Fuzzy Inference System in Macroeconomic Variables Forecasting

Authors: Ε. Giovanis

Abstract:

In this paper we apply an Adaptive Network-Based Fuzzy Inference System (ANFIS) with one input, the dependent variable with one lag, for the forecasting of four macroeconomic variables of US economy, the Gross Domestic Product, the inflation rate, six monthly treasury bills interest rates and unemployment rate. We compare the forecasting performance of ANFIS with those of the widely used linear autoregressive and nonlinear smoothing transition autoregressive (STAR) models. The results are greatly in favour of ANFIS indicating that is an effective tool for macroeconomic forecasting used in academic research and in research and application by the governmental and other institutions

Keywords: Linear models, Macroeconomics, Neuro-Fuzzy, Non-Linear models

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1080724

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[1] Atsalakis, S.G. and G.I. Atsalakis, "Fruit production forecasting by neuro-fuzzy techniques," Paper prepared for presentation at the 113th EAAE Seminar "A resilient European food industry and food chain in a challenging world", Chania, Crete, Greece, September, pp. 3-6, 2009
[2] Ballini, R., I. Luna, L.M. Lima and R.L.F. da Silveira, "A comparative analysis of neurofuzzy, ANN and ARIMA models for Brazilian stock index forecasting," SCE - Computing in Economics and Finance, 1995
[3] D. Mar─ìek, "Stock Price Forecasting: Autoregressive Modelling and Fuzzy Neural Network," Proceedings of the 1999 EUSFLAT-ESTYLF Joint Conference, Palma de Mallorca, Spain, September, pp. 22-25, 1995
[4] 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
[5] D. Kwiatkowski, P. C. B. Phillips, P. Schmidt and Y. Shin, "Testing the Null Hypothesis of Stationarity against the Alternative of a Unit Root", Journal of Econometrics, vol. 54, pp. 159-178, 1992
[6] M. S. Bartlett, "Periodogram analysis and continuous spectra," Biometrika, vol. 37, pp. 1-16, 1950
[7] W.H Greene, Econometric Analysis, Sixth Edition, Prentice Hall, New Jersey, 2008, pp. 560-579
[8] K.S. Chan and H. Tong, "On estimating thresholds in autoregressive models", Journal of Time Series Analysis, vol. 7, pp. 178-190, 1986
[9] T. Teräsvirta and H.M. Anderson, "Characterizing nonlinearities in business cycles using smooth transition autoregressive models", Journal of Applied Econometrics, vol. 7, pp. 119-136, 1992
[10] T. Teräsvirta, C.F. Lin and C.W.J. Granger "Power of the Neural Network Linearity Test", Journal of Time Series Analysis, vol. 14, pp. 209-220, 1993
[11] T. Teräsvirta, "Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models", Journal of the American Statistical Association, vol. 89, no. 425, 208-218, 1994
[12] J.-S.R. Jang, "ANFIS: Adaptive-Network-based Fuzzy Inference Systems," IEEE Trans. on Systems, Man, and Cybernetics, vol. 23, no. 3, pp. 665-685, 1993
[13] J.-S. R. Jang and C.-T. Sun, "Neuro-fuzzy Modeling and Control," Proceedings of the IEEE, vol. 83, no. 3, 378-406, March, 1995
[14] E.H. Moore, "On the reciprocal of the general algebraic matrix," Bulletin of the American Mathematical Society, vol. 26, pp. 394-395, 1920
[15] R. Penrose, "A generalized inverse for matrices," Proceedings of the Cambridge Philosophical Society, vol. 51, pp. 406-413, 1955
[16] M. Petrou, and P. Bosdogianni, Image Processing: The Fundamentals, John Wile & Sons, 2000
[17] L. Khan, S. Anjum and R. Bada, "Standard Fuzzy Model Identification using Gradient Methods," World Applied Sciences Journal, vol. 8, no. 1, pp. 01-09, 2010
[18] M.A. Denai, F. Palis and A. Zeghbib, ANFIS Based Modelling and Control of Non-Linear systems: A Tutorial, IEEE Conf. on Systems, Man, and Cybernetics, vol. 4, pp. 3433-3438, 2004
[19] L.H. Tsoukalas, and R.E. Uhrig, Fuzzy and Neural Approaches in Engineering, First Edition, John Wiley & Sons, 1997, pp. 445-470
[20] J. G. MacKinnon, "Numerical Distribution Functions for Unit Root and Cointegration Tests", Journal of Applied Econometrics, vol. 11, pp. 601- 618, 1996