Includes bibliographical references and indexes.

1. Difference Equations -- 2. Lag Operators -- 3. Stationary ARMA Processes -- 4. Forecasting -- 5. Maximum Likelihood Estimation -- 6. Spectral Analysis -- 7. Asymptotic Distribution Theory -- 8. Linear Regression Models -- 9. Linear Systems of Simultaneous Equations -- 10. Covariance-Stationary Vector Processes -- 11. Vector Autoregressions -- 12. Bayesian Analysis -- 13. The Kalman Filter -- 14. Generalized Method of Moments -- 15. Models of Nonstationary Time Series -- 16. Processes with Deterministic Time Trends -- 17. Univariate Processes with Unit Roots -- 18. Unit Roots in Multivariate Time Series -- 19. Cointegration -- 20. Full-Information Maximum Likelihood Analysis of Cointegrated Systems -- 21. Time Series Models of Heteroskedasticity -- 22. Modeling Time Series with Changes in Regime -- D Greek Letters and Mathematical Symbols Used in the Text.

The last decade has brought dramatic changes in the way that researchers analyze time series data. This much-needed book synthesizes all of the major recent advances and develops a single, coherent presentation of the current state of the art of this increasingly important field.

James Hamilton provides for the first time a thorough and detailed textbook account of important innovations such as vector autoregressions, estimation by generalized method of moments, the economic and statistical consequences of unit roots, time-varying variances, and nonlinear time series models.

In addition, Hamilton presents traditional tools for analyzing dynamic systems, including linear representations, autocovariance, generating functions, spectral analysis, and the Kalman filter, illustrating their usefulness both for economic theory and for studying and interpreting real-world data.

This book is intended to provide students, researchers, and forecasters with a definitive, self-contained survey of dynamic systems, econometrics, and time series analysis. Starting from first principles, Hamilton's lucid presentation makes both old and new developments accessible to first-year graduate students and nonspecialists. Moreover, the work's thoroughness and depth of coverage will make Time Series Analysis an invaluable reference for researchers at the frontiers of the field.

Hamilton achieves these dual objectives by including numerous examples that illustrate exactly how the theoretical results are used and applied in practice, while relegating many details to mathematical appendixes at the end of chapters. As an intellectual roadmap of the field for students and researchers alike, this volume promises to be the authoritative guide for years to come