Time Series Analysis

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Review Time Series Analysis



With the coming of a new modern era in finances and economy comes an equally overwhelmingly new array of analysis in the aspects of time series, which can be a confusing time for graduate students as they seek out where to start. James D. Hamilton’s “Time Series Analysis” narrows down the search by creating these aspects of analysis into a comprehensive yet advanced resource. Considered as a one-stop guide to various and newly-acquired economic analysis inventions such as time-varying instances, and vector autoregressions, “Time Series Analysis” introduces theory to the practicalities of real-world information. Many students and economists consider this book as a much-needed guide for anyone interested in the subject of time series analysis.

About the Author

James Douglas Hamilton (born November 29, 1954) is an American econometrician currently teaching at the University of California, San Diego. His work is especially influential in time series and energy economics. He received his Ph.D. from the University of California, Berkeley, in 1983.

Table of Contents


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

Author Index

Subject Index