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Multivariate Time Series Analysis: With R and Financial Applications (Wiley Series in Probability and Statistics) Hardcover – 28 Jan 2014
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From the Back Cover
An accessible guide to the multivariate time series toolsused in numerous real-world applications
Multivariate Time Series Analysis: With R and FinancialApplications is the much anticipated sequel coming from one ofthe most influential and prominent experts on the topic of timeseries. Through a fundamental balance of theory and methodology, the book supplies readers with a comprehensible approach tofinancial econometric models and their applications to real-worldempirical research.
Differing from the traditional approach to multivariate timeseries, the book focuses on reader comprehension by emphasizingstructural specification, which results in simplified parsimoniousVARMA modeling. Multivariate Time Series Analysis: With R andFinancial Applications utilizes the freely available R softwarepackage to explore complex data and illustrate related computationand analyses. Featuring the techniques and methodology ofmultivariate linear time series, stationary VAR models, VARMA timeseries and models, unit-root process, factor models, andfactor-augmented VAR models, the book includes:
- Over 300 examples and exercises to reinforce the presentedcontent
- User-friendly R subroutines and research presented throughoutto demonstrate modern applications
- Numerous datasets and subroutines to provide readers with adeeper understanding of the material
Multivariate Time Series Analysis is an ideal textbookfor graduate-level courses on time series and quantitative financeand upper-undergraduate level statistics courses in time series.The book is also an indispensable reference for researchers andpractitioners in business, finance, and econometrics.
About the Author
RUEY S. TSAY, PhD, is H.G.B. Alexander Professor of Econometrics and Statistics at The University of Chicago Booth School of Business. He has written over 125 published articles in the areas of business and economic forecasting, data analysis, risk management, and process control. A Fellow of the American Statistical Association, the Institute of Mathematical Statistics, and Academia Sinica, Dr. Tsay is author of Analysis of Financial Time Series, Third Edition and An Introduction to Analysis of Financial Data with R, and coauthor of A Course in Time Series Analysis, all published by Wiley.
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While the explanations are clear, and the derivations are well done, the real value of the book comes from examples showing the implementation of his fabulous MTS package. Using this package, one does not need to have a strong grasp of the underlying math, but rather only a surface understanding of what is going on. Thus, given his abundant sample code makes this book the ideal text for applied technicians who aren't required to memorize esoteric proofs.
The geometry of the models also described in very satisfactory way.
The level is, as far as I see, for graduate studs. The undergraduates may find it very difficult.
I think, the book is the the book that must be handled after a primary source like "Principles of Econometrics (4th ed; Griffith)"
The order by which topics are presented is reasonable. I like that the author starts right off with the more general vector/matrix approach and does not waste time introducing the "special case" posed by univariate time series, since this book is going to be very difficult for a student who has not already been exposed to the univariate case. Indeed, a level of familiarity with advanced mathematical topics (such as infinite-order matrix polynomials) is presumed. I also liked that proofs occur mostly at the end of chapters, and that the author maintains a web page with a listing of errata.
While this is an excellent reference for a variety of techniques, like many of these kinds of books, it does not offer much on the subject of model selection -- that is, the process of choosing between competing techniques, nor much advice on how to determine whether a particular technique might be appropriate or inappropriate in a given setting.
Some reviewers have complained about being turned off by the heavy use of mathematical notation, some of which may seem foreign to some readers. However, I think this is unavoidable when covering advanced topics such as these. To his credit, the author mostly uses a consistent set of notation throughout the book, and is mostly willing to dispense with mathematical purity whenever it would hinder readability. For example, I was thankful that he uses the same symbol for a random variable and its realization, as using separate symbols would have been unnecessarily complicated in this setting.
One of the things that I appreciated about the book is that the proofs are kept to appendix sections at the end of the chapters. I found the level of math approachable. Most of the mathematics is discrete math (summations), without any advanced calculus. Because the book deals with multivariate time series, linear algebra is used through out the book, but again, the level is reasonable. An advanced undergraduate or anyone who has taken applied math graduate courses should have not problem with the math.
Except for some of the notation... Few people read books like this from front to back. Generally there's something that you would like to understand and you read the associated chapters. Jumping into a chapter, I found some of the notation difficult to understand. For example, Prof. Tsay uses B to indicate the back-shift operator (e.g., B(x[t]) = x[t-1]) (see page 19). I have not seen this notation so jumping into a chapter I kept wondering what this B was that I kept seeing in many equations. Some math texts have a page or two that summarizes the local notation. That would have been nice here.
The book covers a variety of topics and in many cases I found the explanation briefer than I would have wished. My favorite time series book is the now dated Modeling Financial Time Series with S-PLUS, Springer (2006) (Prof. Zivot was one of my professors in graduate school). A search will turn this book up in PDF form, so you don't have to buy it. Prof. Zivot's approach works better for me. He introduces a topic and then develops examples, with more discussion. Multivariate Time Series Analysis does provide R examples, but it was difficult for me to understand the jump from theory to example in many cases without pouring over the text several times. One example of this is the coverage of Principle Component Analysis (PCA) for factor models. PCA is introduced and applied to factor models in a few chapters. For me PCA analysis is something that would have been better covered with its own chapter.
Once I got used to Prof. Tsay's notation I found the book easier to understand. Multivariate Time Series Analysis is an important reference and it will join my earlier copy of Prof. Tsay's Analysis of Financial Time Series on my reference shelf.