Amazon.com: Customer reviews: Multivariate Time Series Analysis: With R and Financial Applications

Top positive review

Another winner from Dr. Tsay!

Reviewed in the United States on September 29, 2015

In full disclosure, I took his Financial Time Series class last quarter and thoroughly enjoyed it. As such, I am quite familiar with his teaching style. I bought this book to teach myself VAR models for a project at work.

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.

5 people found this helpful

Top critical review

Critical reviews›

Luis Figueroa

Detailed Analysis of Multivariate Time Series (MTS)

Reviewed in the United States on April 13, 2014

Multivariate Time Series Analysis by Ruey Tsay provides a thorough mathematical analysis of time series and how the open source statistical package R can be used for analysis. The author provides an Add-On package, MTS (Multivariate Time Series) which be installed in R and used for analysis. One great feature of the book is the authors's web page which provides all the R code used in the book and allows the reader a fair amount of practice.

Overall, the book is mathematically dense and challenging to follow. I certainly had a tough time following the numerous equations and terminology. In a future edition, the author may want to include more conceptual descriptions and less equations. The book would also more useful if the author would augment the relatively scarce R program code included in the text, even though he does an excellent job in the website associated with the book.

I would not recommended this book for a casual user who just wants to get an overview of the topic and be able to use the R code to solve specific problems of interest. For maximum benefit, the reader will need significant investment in time to allow carrying out the exercises in R.

Overall, this book is best suited for a user with some familiarity in the mathematical foundations of multivariate time series analysis and wants to better understand advanced topics, and how to use R for their analysis.

7 people found this helpful

Filtered by

4 starClear filter

5 total ratings, 5 with reviews

There was a problem filtering reviews right now. Please try again later.

From the United States

iank

Another essential reference from Prof. Ruey Tsay

Reviewed in the United States on February 20, 2014

Vine Customer Review of Free Product( What's this? )

Professor Ruey Tsay is the author of multiple editions of book on time series analysis, most recently An Introduction to Analysis of Financial Data with R . Most people who work with financial data or who have taken a graduate course in time series analysis have a copy of one of Prof. Tsay's books. The previous books focus largely on univariate time series. Prof. Tsay's new book, Multivariate Time Series Analysis focuses on multiple time series that have some kind of cross sectional relationship. Time series with multivariate or co-integrated distributes occur in finance, which accounts for the sub-title.

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.

7 people found this helpful

Helpful

Mindy

Good reference book for those who have had at least a class in the topic

Reviewed in the United States on March 26, 2014

Vine Customer Review of Free Product( What's this? )

This text is a good reference for a broad range of topics in the analysis of multivariate time series. It covers not only proven mainstream methods (VARMA, cointegration, PCA) but also delves into some cutting-edge techniques such as those used in volatility modelling. The coverage across topics it not uniform, and the author freely admits the depth of coverage has been influenced by his own preferences and understanding of the various topics. Therefore it should come as no surprise that the text is heavily focused on VARMA modelling.

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.

11 people found this helpful

Helpful

Jaewoo Kim

Notations are cryptic. Otherwise, a solid advanced regression book.

Reviewed in the United States on January 25, 2014

Vine Customer Review of Free Product( What's this? )

I do data analytics for a living. I think I know my stats and regression fairly well. I also have a basic understanding of Calculus (It has been years since I took my last Calculus class), which I hardly ever use.

When I opened this book, the notations horrified me. I thought they were straight out of Egyptian hieroglyphics. OK, they were valid math notations, BUT they make the content look FAR MORE intimidating than it really is. The content of this book is nothing more than intermediate to advanced regression. Unfortunately, the author decided to use some intimidating, yet valid, mathematical notations normally used in advanced calculus classes.

A lot of this book is about intuitive and theoretical aspects of regression. It doesn't have too many R examples. If you are trying to learn R, I do not recommend this book. If you are a math major (or similar) and would like to understand the underpinnings of regression better, then this could be the right book for you.

Pros:
1)Explanations and examples of advanced regression are very solid.
2)Makes you think about questions such as "What does P value REALLY mean?".
3)Allows you to apply Time Series in ways you may not have used before (such as testing the validity of the regression model).

Cons:
1)Looks like alien documentation on how to build a spacecraft. OK, it is only advanced math notation with dozen Greek letters and sub-symbols, but that in itself will turn-off many.
2)Definitely not for the beginners in stats or regression.

3 people found this helpful

Helpful

Tyson

Clear, Concise

Reviewed in the United States on February 17, 2014

Vine Customer Review of Free Product( What's this? )

While many of the concepts in this book are much more advanced than the low-level probability and statistics courses I took in college, I was surprised to find that I was able to comprehend several sections with little difficulty. The author does an excellent job of presenting the information with helpful graphs, charts, and clear and concise writing. This is obviously a book designed to be used in a classroom, so some of the concise sections would undoubtedly be easier to understand with a professor's guidance. It would be an easy book to Study, however, because the facts are presented so clearly.

Students, if this book is assigned to your class - get ready for a tough semester, but don't be frightened by the book. There's little to no fluff and thankfully the writing is well laid out so that some of it actually makes sense.

Professors, if you're thinking about assigning this book, please do your students a favor and offer plenty of supplemental notes. Tsay's book is concise, but the brevity makes many sections very difficult to 'teach yourself'. It's easy to find yourself lost in the rapid-fire information.

One person found this helpful

Helpful

From other countries

Peter Fertig, QCR Quantitative Commodity Research Ltd.

An advanced Textbook

Reviewed in Germany on May 5, 2014

Verified Purchase

Ruey Tsay is one of the leading professors on time series Analysis. His "Analysis of Financial Time Series" is already a classical textbook. His Publication "An Introduction to Analysis of Financial Data with R (IAFDR)" is focused on univariate time series. But "Multivariate Time Series Analysis" is not an extension of IAFDR to multivariate time series. It is more a textbook for graduates. It is much more advanced and thus, it requires more mathematical knowledge than IAFDR. Nevertheless, two principles of IAFDR are also applied in this textbook. It provides examples of applying the theoretical concepts to examples of financial or economic time series and it provides the code for the calculations and estimations in R.

Concerning the example code in R, a Major improvement compared with IAFDR is that the code is available at the website of the author, which was not the case when I bought IAFDR. A major advantage is that Mr. Tsay has programmed some functions and procedures in an R package called MTS. Thus, it should be fairly easy to apply multivariate time series models to the data of the user. For quantitative analysts this book is a must read.

2 people found this helpful

Need customer service?

‹ See all details for Multivariate Time Series Analysis: With R and Financial Applications

Multivariate Time Series Analysis: With R and Financial Applications

›

Customer reviews

Customer reviews

Multivariate Time Series Analysis: With R and Financial Applications

Top positive review