Evaluation of an autoregressive process by information measure

The extensive investigations of autoregressive representation in the analysis of a stationary time series have been attracting the attention of many research workers, since the representation is useful in the identification, prediction of the systems and the spectrum estimation of the time series. The problem in fitting an autoregressive model to the observed data lies in the determination of the order of the model. Several methods to estimate the order of the autoregressive model are known, such as the maximum likelihood method or the extended likelihood one, which is called the Final Prediction Error method (FPE). These methods are based on the residual variance, which is composed of the difference between the linear prediction estimate and the observed data. In this paper it is shown that the residual variance is represented by the determinants of the autocorrelation matrix which is derived from applying the amount of information measure to the autoregressive process. This representation is a useful on...