Hamilton, James D. : University of California-San Diego
James D. Hamilton is Professor of Economics at the University of California, San Diego.
Review
"A carefully prepared and well written book. . . . Without doubt, it can be recommended as a very valuable
encyclopedia and textbook for a reader who is looking for a mainly theoretical textbook which combines traditional
time series analysis with a review of recent research areas."
--Journal of Economics
"I am extremely enthusiastic about this book. I think it will quickly become a classic. Like Sargent's
and Varian's texts, it will be a centerpiece of the core cirriculum for graduate students."
--John H. Cochrane, University of Chicago
Submitted by Publisher, April, 2002.
Summary
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.
Table of Contents
Preface
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
A Mathematical Review
B Statistical Tables
C Answers to Selected Exercises
D Greek Letters and Mathematical Symbols Used in the Text
