Smoothing Transition Autoregressive (STAR) Models with Ordinary Least Squares and Genetic Algorithms Optimization

Giovanis, Eleftherios (2008): Smoothing Transition Autoregressive (STAR) Models with Ordinary Least Squares and Genetic Algorithms Optimization.

Abstract

In this paper we present, propose and examine additional membership functions as also we propose least squares with genetic algorithms optimization in order to find the optimum fuzzy membership functions parameters. More specifically, we present the tangent hyperbolic, Gaussian and Generalized bell functions. The reason we propose that is because Smoothing Transition Autoregressive (STAR) models follow fuzzy logic approach therefore more functions should be tested. Some numerical applications for S&P 500, FTSE 100 stock returns and for unemployment rate are presented and MATLAB routines are provided.

• Item Type: MPRA Paper
• Language: English
• Keywords: Smoothing transition; exponential, logistic; Gaussian; Generalized Bell function; tangent hyperbolic; stock returns; unemployment rate; forecast; Genetic algorithms; MATLAB
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63 - Computational Techniques ; Simulation Modeling; C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related Topics; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes

References

Bäck, T. (1996). Evolutionary Algorithms in Theory and Practice. Oxford University Press

Chan, K.S. and Tong, H. (1986). On estimating thresholds in autoregressive models. Journal of Time Series Analysis, Vol. 7, pp. 178-190

Janikow, C. Z., and Michalewicz, Z. (1991). An experimental comparison of binary and floating point representations in genetic algorithms. In R. K. Belew and L. B. Booker, eds., Proceedings of the Fourth International Conference on Genetic Algorithms, Morgan Kaufmann Publishers

Mitchell, M. (1996). An Introduction to Genetic Algorithms. MIT Press Cambridge, Massachusetts, London, England

Teräsvirta, T. (1994). Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models. Journal of the American Statistical Association, Vol. 89, No. 425, pp. 208–218