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

Giovanis, Eleftherios Ordinary Least Squares and Genetic Algorithms Optimization in Smoothing Transition Autoregressive (STAR) Models. International Journal of Computer Information Systems, 2 (3). pp. 17-23. ISSN 2229-5208 (2011)

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Abstract

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: Article
Uncontrolled Keywords: Genetic Algorithms; MATLAB; Membership Functions; Smoothing Transition Autoregressive
Subjects: H Social Sciences > HA Statistics
H Social Sciences > HB Economic Theory
Research Area: Economics and Institutional Change
Depositing User: Ms T. Iannizzi
Date Deposited: 10 Jul 2013 10:42
Last Modified: 10 Jul 2013 10:42
URI: http://eprints.imtlucca.it/id/eprint/1640

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