This new edition of this classic title, now in its seventh edition, presents a balanced and comprehensive introduction to the theory, implementation, and practice of time series analysis. The book covers a wide range of topics, including ARIMA models, forecasting methods, spectral analysis, linear systems, state-space models, the Kalman filters, nonlinear models, volatility models, and multivariate models. It also presents many examples and implementations of time series models and methods to reflect advances in the field.
Highlights of the seventh edition:
A new chapter on univariate volatility models
A revised chapter on linear time series models
A new section on multivariate volatility models
A new section on regime switching models
Many new worked examples, with R code integrated into the text
The book can be used as a textbook for an undergraduate or a graduate level time series course in statistics. The book does not assume many prerequisites in probability and statistics, so it is also intended for students and data analysts in engineering, economics, and finance.
Introduction
Basic Descriptive Techniques
Some Linear Time Series Models
Fitting Time Series Models in the Time Domain
Forecasting
Stationary Processes in the Frequency Domain
Spectral Analysis
Bivariate Processes
Linear Systems
State-Space Models and the Kalman Filter
Non-Linear Models
Volatility Models
Multivariate Time Series Modelling
Some More Advanced Topics
Appendix A Fourier, Laplace, and z-Transforms
Appendix B Dirac Delta Function
Appendix C Covariance and Correlation
Answers to Exercises
