Review Time Series Analysis
by JAMES D. HAMILTON
Description
In recent findings, researchers find new breakthroughs in the areas of financial and economic time series. This book compiles all these recent findings and advances to be presented to undergraduate students. It is a fresh recalling of the most recent updates in the last decade on various aspects of finance and economics. This textbook is essential in the integration of econometrics, updates, and economic theory.
With almost 800 pages, this book is sure to contain a lot of information and knowledge about the modern updates on various aspects of economic and financial time series. The full content of the book is significant, and no paragraph or section is unimportant to the topic. The author does not fail to provide tips on how to avert pitfalls and to attain success.
It is a self-sufficient text on time series analysis with a professional level of mathematical detail. It is highly recommended for individuals and professionals to study time series analysis.
About the Author
JAMES DOUGLAS HAMILTON is a Professor of Economics at the University of California, San Diego.
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
Author Index
Subject Index
