Introductory Time Series with R (Use R) Review

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(More customer reviews)This is a cracking book on applying R to time series analysis. The best parts of the book are all of the worked examples, the accompanying data sets and several different ways to calculate seasonality.
The book is better than most on time series, because it does not neglect the de-trending process needed to get stationery residuals. If you use just the lm() command in R to do this before, then the real gem in this book is the advice to use the gls() command from the nlme library instead (to get the confidence intervals right).
Overall, a very good book that is applied to R but has enough mathematical backing for the techniques presented. However, this is a book about applying time series analysis in R. If you seek a more algebraic treatment, then this is not the book I'm afraid, but it would be a great supplement!

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Analysis of Financial Time Series (Wiley Series in Probability and Statistics) Review

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(More customer reviews)Written by a University of Chicago professor, this book comprehensively covers times series topics relative to investment and trading-oriented finance (i.e., Wall Street money-making machines). Treatment is generally clear and thorough, but an advanced math and stat background is an absolute prerequisite for understanding the materials.
S-Plus/R code is given, but strangely, there is very little on *why* and
*when* one uses each of the techniques. Under what cirmcustances should I use or not use GARCH? What exactly is PCA good for in real-world applications? These important questions are not answered, in other words, you don't get a sense of the real-world context for these topics.


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Provides statistical tools and techniques needed to understand today's financial markets
The Second Edition of this critically acclaimed text provides a comprehensive and systematic introduction to financial econometric models and their applications in modeling and predicting financial time series data. This latest edition continues to emphasize empirical financial data and focuses on real-world examples. Following this approach, readers will master key aspects of financial time series, including volatility modeling, neural network applications, market microstructure and high-frequency financial data, continuous-time models and Ito's Lemma, Value at Risk, multiple returns analysis, financial factor models, and econometric modeling via computation-intensive methods.
The author begins with the basic characteristics of financial time series data, setting the foundation for the three main topics:
Analysis and application of univariate financial time series
Return series of multiple assets
Bayesian inference in finance methods

This new edition is a thoroughly revised and updated text, including the addition of S-Plus® commands and illustrations. Exercises have been thoroughly updated and expanded and include the most current data, providing readers with more opportunities to put the models and methods into practice. Among the new material added to the text, readers will find:
Consistent covariance estimation under heteroscedasticity and serial correlation
Alternative approaches to volatility modeling
Financial factor models
State-space models
Kalman filtering
Estimation of stochastic diffusion models

The tools provided in this text aid readers in developing a deeper understanding of financial markets through firsthand experience in working with financial data. This is an ideal textbook for MBA students as well as a reference for researchers and professionals in business and finance.

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Forecasting and Time Series: An Applied Approach (Forecasting & Time) Review

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(More customer reviews)I reviewed the third edition of this book for the American Statistician in 1994. The book covers most of the important topics for an applied course and has a reasonable list of references. There are many examples and homework exercises. Statistical software packages such as SAS and MINITAB are used throughout in example problems. The early chapters cover the basics of statistical inference and regression (Chapters 2-5). This material can be skipped in a first time series course if introductory statistics is a prerequisite.
The latter chapters cover time series regression, seasonal decomposition methods, exponential smoothing and Box-Jenkins methods. But this book does not include nonlinear time series models and it overlooks the recent and popular state space approach to time series modeling. Multivariate time series methods are also left out, though perhaps they are more appropriate for an advanced or second course in time series analysis.
The cookbook nature of the text can be found in the guidelines given for Box-Jenkins model identification. The statistical theory that the methods rely on is avoided. Although a number of important probability distributions are used with their relevant statistical tables, the underlying assumptions and distributional theory is completely avoided.
Important concepts such as the central limit theorem and the concept of a stationary stochastic process are given only very brief treatment. Other concepts are oversimplified to avoid the need for the development of any distribution theory.
This book will serve well for a course in which the student is interested in how to implement exponential smoothing and the general class of Box-Jenkins models through the use of standard statistical packages. However if the instructor wants depth of understanding the text is not adequate. Frequecy domain methods often useful in engineering applications are not even discussed.
While the book covers forecasting applications, it does not consider applications to decomposition of variance or discriminant analysis. Time series methods are also applicable in these contexts. Abraham and Ledolter (1984) "Statistical Methods for Forecasting" cover the same topics but in much greater depth. Also Janacek and Swift (1993) "Time Series: Forecasting, Simulation, Applications" is slightly more advanced and provides broader coverage. Anyone interested in the theory can consult a number of good books including the latest edition of Brockwell and Davis "Time Series: Theory and Methods". Shumway and Stoffer (2000) "Time Series Analysis and Its Applications" is up-to-date, comprehensive and has many good engineering applications.


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Applied Econometric Times Series (Wiley Series in Probability and Statistics) Review

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(More customer reviews)I bought Walter Enders book several years ago, when I was an undergraduate student. It's a nice manual. Perhaps you won't see the statistical demonstration of the unit-root (Dickey-Fuller) test, but you will understand why it doesn't follow a standard probability distribution and you'll know how to use it. It's the same idea with Perron's unit-root with structural change test. The author introduces the reader to the main topics of interest in the time series field; ARIMA, VAR, ARCH, unit roots, cointegration, and distinction between deterministic trends and stochastic trends. This work is done through an understandable and fun text. You will enjoy reading the book. Besides that, the author illustrates each topic with an economic example perfectly presented and, in general, very interesting (business cycles, PPP, foreign exchange Market efficiency, Unit roots in GNP for example). I particularly enjoyed the unit root and the perron's test chapters. I used them a lot in my final work in college. Here, you will have the simplest explanation of ARCH processes. As someone else said, this is only an introductory book (for applied econometricians it should be seen as an excellent and very intuitive cookbook); if you are interested in time series, you can begin here, but you should then reading more advanced books, such as Hamilton's Time Series Analysis. A great combination of introductory manuals can be achieved if you have Johnston and Dinardo "Econometric models".

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Modeling Financial Time Series with S-PLUS® Review

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(More customer reviews)This is an excellent book on financial econometrics, very practical yet rigorous. I wish all econometrics/statistics textbook could like this. Basic theory followed by practical examples - real life examples, not simplified ones like in other books. The authors gave detailed instructions on how to implement various econometric models, i.e. multi-factor models, GARCH, MGARCH, long memory models, state-space, etc. Most econometrics textbooks are at two extremes, they are either too theoretical (you still don't know how to put those models in real life), or too simple (lack of mathematical rigor and without advanced applications). This book is a combination of both worlds, computer codes/math models, and real life examples (some really good ones). A lot of cutting-edge techniques and advanced topics are also covered.

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This book represents an integration of theory, methods, and examples using the S-PLUS statistical modeling language and the S+FinMetrics module to facilitate the practice of financial econometrics. It is the first book to show the power of S-PLUS for the analysis of time series data. It is written for researchers and practitioners in the finance industry, academic researchers in economics and finance, and advanced MBA and graduate students in economics and finance.Readers are assumed to have a basic knowledge of S-PLUS and a solid grounding in basic statistics and time series concepts. This edition covers S+FinMetrics 2.0 and includes new chapters.

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Time Series: Theory and Methods (Springer Series in Statistics) Review

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(More customer reviews)Of course, this an advanced textbook on Time Series. The reader is supposed to have been introduced to the subject, and certainly is looking for a more theoretical treatment.
If you want to learn time series for the first time, this is not the book.
If you want a friendly book, do not see springer's publications.
However, if you want a fair rigourous book, you have found it.
I think the exercises are illustrative, but sometimes long.

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New Introduction to Multiple Time Series Analysis Review

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(More customer reviews)If you are looking for a book on VARs and cointegration, this is it.
Very clearly written, and with numerical applications of every new concept (so that you can check the accuracy of your codes ...)
Its a significantly improved version of the last edition.
Highly recommended.


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This is the new and totally revised edition of Lütkepohl's classic 1991 work. It provides a detailed introduction to the main steps of analyzing multiple time series, model specification, estimation, model checking, and for using the models for economic analysis and forecasting. The book now includes new chapters on cointegration analysis, structural vector autoregressions, cointegrated VARMA processes and multivariate ARCH models. The book bridges the gap to the difficult technical literature on the topic. It is accessible to graduate students in business and economics. In addition, multiple time series courses in other fields such as statistics and engineering may be based on it.

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