Order identification for Gaussian moving averages using the codifference function

摘要

In the traditional Box-Ienkins modelling procedure, we use the sample autocorrelation function as a tool for identifying the plausible models for empirical data. In this paper, we consider the sample normalized codifference as a new tool for the preliminary order identification of pure univariate Gaussian moving average process. From simulation studies, we find that when the number of observations are large (more than 100), the proposed method may be superior than a similar identification procedure which is based on the sample autocorrelation function. Simulation results also indicate that the proposed method may perform as well as Gallagher's procedure [Gallagher, C., 2002, Order identification for Gaussian moving averages using the Covariation. Journal of the Statistical Computation and Simulations, 72(4), 279-283.].