Time Series Analysis

Now in its fifth edition, this text on forecasting and control combines long-established modeling methods with material from the last decade. It shows how to build, classify, test, and analyze stochastic models for time series, and it illustrates the work with examples from business, finance, economics, and engineering. The authors also explain five practical applications: forecasting, determining a system's transfer function, measuring the effects of intervention events, developing multivariate dynamic models, and designing simple control schemes.

The revised chapter on multivariate time series analysis expands its treatment of vector autoregressive (VAR) models and introduces the analytical tools needed to model vector series. A broadened chapter on special topics covers unit root testing, time-varying volatility models such as ARCH and GARCH, nonlinear time series models, and long memory models. Chapter introductions have been streamlined, the references have been updated, and new end-of-chapter exercises have been added.

The edition uses the free R software for graphics and calculations, with scripts that demonstrate model building and forecasting. It is intended as a reference for researchers and practitioners in time series analysis, econometrics, finance, and related fields, and as a textbook for beginning graduate courses in statistics, mathematics, economics, finance, engineering, and physics.