ABSTRACT
Among the stationary time series models discussed in the preceding chapter, very efficient estimation methods can be derived for Aggressive (AR) models. This chapter presents methods for estimating the parameters of the AR model by the Yule-Walker method, the least squares method and the partial autocorrelation (PARCOR) methods. The Yule-Walker method and the least squares method for the estimation and identification of the multivariate AR model are shown. When a multivariate AR model is given, the cross-covariance function is obtained. On the other hand, using these equations, the estimates of the parameters of the multivariate AR model can be obtained through the sample cross-covariance function. For actual computation, similarly to the univariate AR model, there is a computationally efficient algorithm.
