Time Series Analysis
James D. Hamilton

One of Princeton University Press's Notable Centenary Titles.

Cloth | 1994 | $105.00 / £53.00
820 pp. | 6 x 9

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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.

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

Endorsement:

"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

Table of Contents

This book has been translated into:

• Japanese

Subject Areas:

• Economics
• Finance

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File created: 10/18/2009

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