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Summary:
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.
Customer Reviews:
Average Customer Rating:
Excellent Book
Customer Rating:
Its an excellent book to study advanced time series and for theoretical understanding of Time series. A must read at the graduate level.
Thanks Mr. Hamilton for writing such an excellent book
time series for econometricians especially
Customer Rating:
This is a large text in time series analysis that is designed for graduate students as the author acknowledges in his preface. It deals primarily with the theory and the tools rather than providing practical applications. It does not require a Ph.D. but does require a fair amount of mathematical sophistication that comes from advanced courses in probability and statistics. There are many good books at this level. This one has some unique features. It covers the traditional ARIMA models that can be found in most texts and uses the operator notation that Box and Jenkins introduced. It adds vector autoregressions which is fairly recent material. Spectral analysis (the frequency domain approach)is also covered and asymptotic theory is presented. Linear systems (more common to econometric time series than in the standard statistical books) is covered. Topics not commonly covered in competitor texts include nonstationary cases (both univariate and multivariate)with unit roots to the characteristic equation, Bayesian approaches, heteroscedastic models including the ARCH models and the topic of cointegration originally developed by Clive Granger. The book is loaded with references to the literature and is slanted towards methods useful in econometrics. Other good books at this level include Brockwell and Davis (1987), Fuller (1976), Anderson (1971), Harvey (1981) and Shumway and Stoffer (2000). Good texts solely in the frequency domain include Bloomfeld (1976), Priestley (1981), Koopmans (1974) and Brillinger (1981). Box, Jenkins and Reinsel (1994) provides practical applications using the Box-Jenkins time domain approach.
Well, it's the same thickness as a Bible.
Customer Rating:
I purchased "Time Series Analysis" after reading that this was the time series "Bible". This book is certainly as long as a Bible, yet, paradoxically, it doesn't seem to contain very much useful information. Every time I look something up in it, its not there.
This book is almost 800 pages long and contains chapters on all of the main areas of time series analysis, including stationary ARMA models, VAR models, ARCH models, cointegration, etc. Hamilton gives all of the relevant proofs and algebra relating to these areas. However, he provides no worked examples to illustrate these concepts, which makes them very difficult to absorb. Hamilton provides some exercises at the end of each chapter (with solutions at the end of the book), but not enough to develop a working knowledge of this material.
I purchased this book because I needed to fit time series models as part of my PhD thesis and because I teach a unit on time series analysis to final-year undergraduate students. I have found this book completely useless for both of these purposes. At the same time I purchased this book, I also purchased Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics) and Econometric Analysis (5th Edition) and I have found both of these books to be infinitely more helpful.
Excellent book in time series
Customer Rating:
I don't think there is another book out ther that would outperform this book in time series econometrics. A must have if you are a graduate student in economics.
No complaints.
Customer Rating:
No complaints. I received the book before deadline and book is same as descrition. 100% recomended seller
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