Estimation of fixed points for nonlinear time series |
| |
Authors: | Shapour Mohammadi Hossein Abbasinezhad |
| |
Institution: | Faculty of Management and Faculty of Economics, University of Tehran, Tehran, Iran |
| |
Abstract: | Identification of fixed points is very important in dynamic systems analysis. One method used is based on polynomial regression. In this article, we show that methods other than that of Aguirre and Souza can be more accurate if the classical assumptions for regression are violated. Simulation results reveal that an artificial neural network (ANN) is more precise than the Aguirre and Souza method, which is based on cluster expansion method. Overall, ANN is the best method for finding fixed (equilibrium) points of nonlinear time series, followed by nonparametric regression in terms of accuracy. For larger sample sizes, ANN estimates are generally accurate and the method is robust to changes in the signal/noise ratio. © 2013 Wiley Periodicals, Inc. Complexity 19: 30–39, 2014 |
| |
Keywords: | artificial neural networks fixed points cluster expansion generalized maximum entropy nonparametric regression robust regression |
|
|