Research Article 
On the Comparative Performance of Pure Vector Autoregressive-Moving Average and Vector Bilinear Autoregressive-Moving Average Time Series Models
Asian Journal of Mathematics & Statistics, 2009, 2(2), 33-40.
Abstract
This study was motivated by the need to establish a vector form of autoregressive moving average (VARMA) models comprising linear and non linear components that could compete with the pure vector linear VARMA models. General bilinear vector autoregressive moving average (BIVARMA) was established as an extension of the univariate bilinear model. Three revenue series identified as autoregressive (AR) and Moving Average (MA) processes on the basis of the distribution of autocorrelation and partial autocorrelation functions were used to illustrate the performances of the two competing vector forms in terms of estimates and residual variances. Graphical comparisons were also made. The results showed that BIVARMA models established perform best and provide better estimates than the VARMA models.
ASCI-ID: 6-25
Related Articles
Method of Estimating Missing Values in a Stationary Autoregressive (AR) Process
Asian Journal of Mathematics & Statistics, 2016, 9(1-3), 6-10.
Method of Estimating Missing Values in a Stationary Autoregressive (AR) ProcessAsian Journal of Mathematics & Statistics, 2016, 9(1-3), 6-10.