The Performance of AlCC as an Order Selection Criterion in ARMA Time Series Models

This study is undertaken with the objective of investigating the performance of Akaike's Information Corrected Criterion (AlCC) as an order determination criterion for the selection of Autoregressive Moving-Average or ARMA (P,q) time series model. A simulation investigation was carried to determine the probability of the AlCC statistics picking up the correct model. Result obtained showed that the probability of the AlCC criterion picking up the correct model was moderately good. The problem of over parameterization existed but under parameterization was found to be minimal. Hence, for any two comparable models, it is always safe to choose the one with lower order of p and q

Fractionally Integrated Separable Spatial Autoregressive (FISSAR) Model and Some of Its Properties

Spatial modelling has its applications in many fields. In time-series there exist a class of models known as long memory models where the autocorrelation function decays rather slowly. These types of time-series data are modelled as fractionally integrated ARMA processes. Spatial data may also exhibit a long memory structure and in order to model such a structure we introduce a new class of models called the fractionally integrated separable spatial autoregressive (FISSAR) model and discuss some of its properties. One way of estimating the parameters of the FISSAR model is also discussed in this article