Time Series Analysis Hardcover – 31 Jan 1994
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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
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
"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
-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
From the Back Cover
The last decade has brought dramatic changes in the way that researchers analyze time series data. This much-needed book synthesizes all of the major recent advances and develops a single, coherent presentation of the current state of the art of this increasingly important field. James Hamilton provides for the first time a thorough and detailed textbook account of important innovations such as vector autoregressions, estimation by generalized method of moments, the economic and statistical consequences of unit roots, time-varying variances, and nonlinear time series models. In addition, Hamilton presents traditional tools for analyzing dynamic systems, including linear representations, autocovariance, generating functions, spectral analysis, and the Kalman filter, illustrating their usefulness both for economic theory and for studying and interpreting real-world data. This book is intended to provide students, researchers, and forecasters with a definitive, self-contained survey of dynamic systems, econometrics, and time series analysis. Starting from first principles, Hamilton's lucid presentation makes both old and new developments accessible to first-year graduate students and nonspecialists. Moreover, the work's thoroughness and depth of coverage will make Time Series Analysis an invaluable reference for researchers at the frontiers of the field. Hamilton achieves these dual objectives by including numerous examples that illustrate exactly how the theoretical results are used and applied in practice, while relegating many details to mathematical appendixes at the end of chapters. As an intellectual roadmap of the field for students and researchers alike, this volume promises to bethe authoritative guide for years to come.|
"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 ChicagoSee all Product description
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It was also originally published in 1994 so might be out-of-date if you're looking for more 'cutting edge' stuff. The format is also a bit dated with more emphasis of the maths than the examples and text as per more modern textbooks.
I'd recommend this to people who have some experience already in time series and want a comprehensive reference book as I think it could be quite confusing to someone new to the topic. Not by any stretch a bad book but neither is it the best.
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.
Arguments are clear and fully explained, and, providing a decent mathematical background, can work for self-study.
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