Time Series: Theory and Methods (Springer Series in Statistics) - Hardcover

This study gives an account of linear time series models and their application to the modelling and prediction of data collected sequentially in time. The aim is to provide specific techniques for handling data and at the same time to instill an understanding of the mathematical basis behind these techniques. The text contains chapters on multivariate series and state-space models (including applications of the Kalman recursions to missing-value problems) and shorter accounts of special topics including long-range dependence, infinite variance processes and non-linear models. Distinctive features of the book are the extensive use of elementary Hilbert space methods and recursive prediction techniques based on innovations, use of the exact Gaussian likelihood and AIC for inference, treatment of the asymptotic behaviour of the maximum likelihood estimators of the coefficients of univariate ARMA models, illustrations of the techniques by means of numerical examples, and a number of problems for the reader.