Harvey is a pioneer in the field who is well-known for his work applying the Kalman filter to time series modeling. In the opening three chapters of this book, he provides a sophisticated treatment of ARMA models and applicable estimation techniques. In chapter 4, he covers state-space models and the Kalman filter. Chapter 5 confronts the challenges of modeling non-stationary processes, and pursues three different solutions. Several models and techniques are introduced, including the classic Box-Jenkins differencing approach and the more sophisticated structural approach. The last three chapters cover the frequency domain, multivariate models and non-linear models, including ARCH and stochastic variance models. This is an exceptional book that opens the door to the vast literature on time series analysis. The book is technical, but it will be accessible to anyone who has mastered basic concepts of statistics as presented by Casella and Berger (2001).