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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
Product Description
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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Most Helpful Customer Reviews
7 of 7 people found the following review helpful
Format:Hardcover
At Oxford University, one of the professors compared Time Series Analysis to The Holy Bible, implying that it has everything a first year economics graduate student needs for a time series course. Personally, I have found TSA to be incredibly comprehensive and I use it as a textbook, dictionary and reference book! I strongly recommend it to econometricians and macroeconomists.
5 of 5 people found the following review helpful
Format:Hardcover
This is a fantastic book and should be the starting place for all work in the area of time series, but now I want more.
First I think the Multivariate sections are two brief and the derivation of the Multivariate central limit theorem fails to note several important details that a text such as this should carry, Davidson's slightly intractable but very complete Stochastic Limit Theory does a better job, as all grad students of time series should have some knowledge of the cramer wold device and its uses. Bayesian VAR's are also the rage now in forecasting and a little more coverage of the advances in this area would be useful, if a new edition ever comes out.
For those who criticise the notation, this is the easiest book of it's type and if this is too hard then you need to really take a step back, try reading Magnus and Neudecker, Muirhead or Kollo and Von Rosen, these are really awful examples of something that is not hard, made hard, Hamilton by comparison is an example of simplicity in action, with a coherent notation structure and very useful annotations, this stuff isn't easy, if it was everybody would do it, as there is money in this art.
In summary if you are studying at the PhD level and analysing data which you believe is generated via some dynamic stochastic process then you need this book, simple as that.
PS Just as an afterthought anybody who finds implementing these ideas difficult should have a look at the spatial-econometrics toolbox for matlab, by James LeSage, covers basically everything in this book and more besides.
First I think the Multivariate sections are two brief and the derivation of the Multivariate central limit theorem fails to note several important details that a text such as this should carry, Davidson's slightly intractable but very complete Stochastic Limit Theory does a better job, as all grad students of time series should have some knowledge of the cramer wold device and its uses. Bayesian VAR's are also the rage now in forecasting and a little more coverage of the advances in this area would be useful, if a new edition ever comes out.
For those who criticise the notation, this is the easiest book of it's type and if this is too hard then you need to really take a step back, try reading Magnus and Neudecker, Muirhead or Kollo and Von Rosen, these are really awful examples of something that is not hard, made hard, Hamilton by comparison is an example of simplicity in action, with a coherent notation structure and very useful annotations, this stuff isn't easy, if it was everybody would do it, as there is money in this art.
In summary if you are studying at the PhD level and analysing data which you believe is generated via some dynamic stochastic process then you need this book, simple as that.
PS Just as an afterthought anybody who finds implementing these ideas difficult should have a look at the spatial-econometrics toolbox for matlab, by James LeSage, covers basically everything in this book and more besides.
15 of 18 people found the following review helpful
Format:Hardcover
"Time Series Analysis" covers a wide range of topics and would be most useful as a reference to people working with financial time series. Apparently it also aims to be useful to students, and here it fails. The mathematics required is not inherently difficult (mostly just linear algebra), but is made to seem so by the lack of attention which the author gave to clarity and organisation.
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Most Recent Customer Reviews
A classic, complete with information
This book is simply THE book on time series analysis. Very well written and complete. I dont give it 5 stars only because the printing quality is a little bit poor. Read more
Published on 16 Aug 2009 by Juan Mendoza
Extraordinary handbook!
My dear colleagues of Amazon.... Maybe somebody told you that this book is incredible because it is very good or because it is too mathematical. Read more
Published on 15 July 1999
An overview of the current state of Time Series Analysis.
This book offers an excellant overview of the current state of Time Series Analysis at a level that most graduate students should find very understandable. Read more
Published on 22 Feb 1998
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