Giovanis, Eleftherios (2008): Additional Smoothing Transition Autoregressive Models.
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Abstract
In this paper we present, propose and examine additional membership functions. There is no reason why more functions cannot be proposed. More specifically, we present the tangent hyperbolic, Gaussian and Generalized bell functions. Because Smoothing Transition Autoregressive (STAR) models follow fuzzy logic approach, more functions should be tested. Furthermore, fuzzy rules can be incorporated or other training or computational methods can be applied as the error backpropagation algorithm instead to nonlinear squares. Some numerical applications are presented and MATLAB routines are provided
Item Type: | MPRA Paper |
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Original Title: | Additional Smoothing Transition Autoregressive Models |
Language: | English |
Keywords: | Smoothing transition; exponential, logistic; Gaussian, Generalized Bell function; tangent hyperbolic; stock returns; unemployment rate; forecast; MATLAB |
Subjects: | 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 B - History of Economic Thought, Methodology, and Heterodox Approaches > B5 - Current Heterodox Approaches > B53 - Austrian |
Item ID: | 24657 |
Depositing User: | Eleftherios Giovanis |
Date Deposited: | 29 Aug 2010 09:14 |
Last Modified: | 26 Sep 2019 15:48 |
References: | Chan, K.S. and Tong, H. (1986). On estimating thresholds in autoregressive models. Journal of Time Series Analysis, Vol. 7, pp. 178-190 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 |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/24657 |