The Smooth Transition Autoregressive (STAR) models are becoming popular in modeling economic and financial time series. The asymmetric type of the model is the Logistic STAR (LSTAR) model, which is limited in its applications as a result of its asymmetric property, which makes it suitable for modelling specific macroeconomic time series. This study was designed to develop the Absolute Error LSTAR (AELSTAR) and Quadratic LSTAR (QLSTAR) models for improving symmetry and performance in terms of model fitness. Modified Teräsvirta’s Procedure (TP) and Escribano and Jordá's Procedure (EJP) were used to test for nonlinearity in the series. The performance of the AELSTAR and QLSTAR models showed that TP and EJP realized time series with improved symmetry as indicated by the lower relative frequencies than that realized with the existing LSTAR model. The AELSTAR model performed better than QLSTAR model at higher nonlinearity, and the selection of both models showed evidence of asymptotic property. The AELSTAR and QLSTAR models showed improved symmetry over the existing asymmetric LSTAR model.
Yaya, OlaOluwa S. and Shittu, Olanrewaju I.
"Symmetric Variants of Logistic Smooth Transition Autoregressive Models: Monte Carlo Evidences,"
Journal of Modern Applied Statistical Methods: Vol. 15
, Article 35.
Available at: http://digitalcommons.wayne.edu/jmasm/vol15/iss1/35