Adaptive Neuro-Fuzzy Inference Systems with Heteroscedastic Errors for
##plugins.themes.bootstrap3.article.details##
Abstract
This paper proposes a new kind of non-linear hybrid model. In the proposed model, mean non-linearity is represented by using an adaptive neuro-fuzzy inference system (ANFIS) whereas variance is represented using a conditional self-regressive heteroscedastic component. The mathematical formula for this type of model is shown and a method to estimate it is proposed. In addition, a specification strategy is developed for the proposed model, based on a battery of statistical soft transaction regression (STR) tests and on verosimility radius testing. As a case study, the IBM stock closing price series dynamics were modeled, which is commonly used as a benchmark in the literature on time series. Results indicate that the model developed represents the dynamics of the studied series better than other models with similar characteristics.
Keywords
ANFIS, ARCH, hetere cedasticidade, séries temporais, modelos não lineaisANFIS, ARCH, heteroscedasticity, time series, non-linear modelsANFIS, ARCH, heterocedasticidad, series temporales, modelos no lineales
References
How to Cite
Zapata Gómez, E. C., Velásquez Henao, J. D., & Smith Quintero, R. A. (2008). Adaptive Neuro-Fuzzy Inference Systems with Heteroscedastic Errors for. Cuadernos De Administración, 21(37). Retrieved from https://revistas.javeriana.edu.co/index.php/cuadernos_admon/article/view/3909
Issue
Section
Artículos