Time Series Analysis / Edition 1

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Overview

The last decade has brought dramatic changes in the way that researchers analyze economic and financial time series. This book synthesizes these recent advances and makes them accessible to first-year graduate students. James Hamilton provides the first adequate text-book treatments of important innovations such as vector autoregressions, generalized method of moments, the economic and statistical consequences of unit roots, time-varying variances, and nonlinear time series models. In addition, he presents basic tools for analyzing dynamic systems (including linear representations, autocovariance generating functions, spectral analysis, and the Kalman filter) in a way that integrates economic theory with the practical difficulties of analyzing and interpreting real-world data. Time Series Analysis fills an important need for a textbook that integrates economic theory, econometrics, and new results.

The book is intended to provide students and researchers with a self-contained survey of time series analysis. It starts from first principles and should be readily accessible to any beginning graduate student, while it is also intended to serve as a reference book for researchers.

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Editorial Reviews

Journal of Economics
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.
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Product Details

  • ISBN-13: 9780691042893
  • Publisher: Princeton University Press
  • Publication date: 1/11/1994
  • Edition description: New Edition
  • Edition number: 1
  • Pages: 820
  • Sales rank: 999,642
  • Product dimensions: 6.98 (w) x 10.08 (h) x 2.17 (d)

Table of Contents

Preface
1 Difference Equations 1
2 Lag Operators 25
3 Stationary ARMA Processes 43
4 Forecasting 72
5 Maximum Likelihood Estimation 117
6 Spectral Analysis 152
7 Asymptotic Distribution Theory 180
8 Linear Regression Models 200
9 Linear Systems of Simultaneous Equations 233
10 Covariance-Stationary Vector Processes 257
11 Vector Autoregressions 291
12 Bayesian Analysis 351
13 The Kalman Filter 372
14 Generalized Method of Moments 409
15 Models of Nonstationary Time Series 435
16 Processes with Deterministic Time Trends 454
17 Univariate Processes with Unit Roots 475
18 Unit Roots in Multivariate Time Series 544
19 Cointegration 571
20 Full-Information Maximum Likelihood Analysis of Cointegrated Systems 630
21 Time Series Models of Heteroskedasticity 657
22 Modeling Time Series with Changes in Regime 677
A Mathematical Review 704
B Statistical Tables 751
C Answers to Selected Exercises 769
D Greek Letters and Mathematical Symbols Used in the Text 786
Author Index 789
Subject Index 792
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Recipe

"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

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