The main subject of this book is empirical application of multivariate linear time series model on quarterly or month- ly economic data to discoverand describe important dynamic relationships between the variables of interest. The book stresses "real-life" application and the selection of data analytic tools. Simple numerical examples and some more al- gebraicexercises are used to illustrate major points. Rele- vant old and recent results from over 400 authors and refe- rences from econometrics, mathematical statistics, time se- ries analysis, economics and descriptve statistics are dis- cussed. Appropriate use of multivariate time series models requires an intimate knowledge of relevant characteristics of thedata.One can obtain this using a method that combines influence analysis (which data points contain the major part of the information?) and diagnostic checking (does the model describe the interesting part of the information well enough?). For economic time series these issuses are (the type of) nonstationarity of the trend and seasonal compo- nent, be it of the (fractional) "unit root" type or of the changing parameter type (structural breaks), both in a unva- riate and a multivariate context. The book introduces new graphical and statistical methodes to improve the understan- ding of seasonality, outliers, structural breaks, pushing trends and pulling equilibria in aparticular data set
