A Course in Time Series Analysis / Edition 1

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New statistical methods and future directions of research in time series

A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, and signal extraction. They then move on to advanced topics, focusing on heteroscedastic models, nonlinear time series models, Bayesian time series analysis, nonparametric time series analysis, and neural networks. Multivariate time series coverage includes presentations on vector ARMA models, cointegration, and multivariate linear systems. Special features include:

  • Contributions from eleven of the world's leading figures in time series
  • Shared balance between theory and application
  • Exercise series sets
  • Many real data examples
  • Consistent style and clear, common notation in all contributions
  • 60 helpful graphs and tables

Requiring no previous knowledge of the subject, A Course in Time Series Analysis is an important reference and a highly useful resource for researchers and practitioners in statistics, economics, business, engineering, and environmental analysis.

An Instructor's Manual presenting detailed solutions to all the problems in the book is available upon request from the Wiley editorial department.

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Editorial Reviews

From the Publisher
"This text demonstrate how to build time series models for univariate and multivariate time series data." (SciTech Book News, Vol. 25, No. 2, June 2001)

"...material is thoroughly and carefully presented...a very useful addition to any collection both for learning and reference." (Short Book Reviews, Vol. 21, No. 2, August 2001)

"From the preface: 'The book can be used as a principal text or a complementary text for courses in time series.?" (Mathematical Reviews, Issue 2001k)

"...an excellent complement...for a first graduate course in time series analysis...a nice addition to anyone's time series library." (Technometrics, Vol. 43, No. 4, November 2001)

"If you are familiar with the basics...and need a compass to navigate the vast world of time series literature, then this book is certainly what you need to have around...presents seamlessly and coherently overviews of the current status of time series research and applications." (The American Statistician, Vol. 56, No. 1, February 2002)

"...an excellent source of introductory surveys of several timely topics in time series analysis..." (Statistical Papers, July 2002)

"...a nice compendium covering a lot of relevant material..." (Statistics & Decisions, Vol.20, No.4, 2002)

SciTech Book
This text demonstrate how to build time series models for univariate and multivariate time series data.
SciTech Book
This text demonstrate how to build time series models for univariate and multivariate time series data.
The time series, a sequence of observations taken at regular intervals, is frequently used to organize data in business, economics, engineering, the environment, medicine, and other areas; examples include daily stock prices, weekly traffic volume, and annual growth rates. This text demonstrates how to build time series models for univariate and multivariate time series data. It covers basic concepts , such as ARIMA models, the Kalman filter, and signal extraction, as well as more advanced topics including heteroscedastic models, nonlinear time series models, and Bayesian time series analysis. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

Meet the Author

DANIEL PE'A, PhD, is Professor of Statistics, Universidad Carlos III de Madrid.

GEORGE C. TIAO, PhD, is W. Allen Wallis Professor of Statistics and Econometrics, Graduate School of Business, University of Chicago.

RUEY S. TSAY, PhD, is H. G. B. Alexander Professor of Statistics and Econometrics, Graduate School of Business, University of Chicago.

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Table of Contents

Introduction (D. Peña & G. Tiao).


Univariate Time Series: Autocorrelation, Linear Prediction, Spectrum, State Space Model (G. Wilson).

Univariate Autoregressive Moving Average Models (G. Tiao).

Model Fitting and Checking, and the Kalman Filter (G. Wilson).

Prediction and Model Selection (D. Peña).

Outliers, Influential Observations and Missing Data (D. Peña).

Automatic Modeling Methods for Univariate Series (V. Gomez & A. Maravall).

Seasonal Adjustment and Signal Extraction in Economic Time Series (V. Gomez & A. Maravall).


Heteroscedatic Models (R. Tsay).

Nonlinear Time Series Models (R. Tsay).

Bayesian Time Series Analysis (R. Tsay).

Nonparametric Time Series Analysis: Nonparametric Regression, Locally Weighted Regression, Autoregression and Quantile Regression (S. Heiler).

Neural Networks (K. Hornik & F. Leisch).


Vector ARMA Models (G. Tiao).

Cointegration in the VAR Model (S. Johansen).

Multivariate Linear Systems (M. Deistler).



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This book is based on the lectures of the ECAS' 97 Course in Time Series Analysis held at El Escorial, Madrid, Spain, from September 15 to September 19, 1997. The course was sponsored by the European Courses in Advanced Statistics (ECAS). In accordance with the objectives of ECAS, the lectures are directed to both researchers and teachers of statistics in academic institutions and statistical professionals in industry and govermment, with the goal of presenting an overview of the current status of the area. In particular, different approaches to time series analysis are discussed and compared. In editing the book, we have worked hard to uphold ECAS' objectives. In addition, special efforts have been made to unify the notation and to include as many topics as possible, so that readers of the book can have an overview of the current status of time series research and applications.

The book consists of three main components. The first component concern basic materials of univariate time series analysis presented in the first eight chapters. It includes recent developments in outlier detection, automatic model selection, and seasonal adjustment. The second component addresses advanced topics in univariate time series analysis such as conditional heteroscedastic models, nonlinear models, Bayesian analysis, nonparametric methods, and neural networks. This component represents current research activities in univariate time series analysis. The third and final component of the book concerns with multivariate time series, including vector ARMA models, cointegration, and linear systems.

The book can be used as a principal text or a complementary text for courses in time series. A basic time series course can be taught from the first part of the book that presents the basic material that can be found in the standard texts in time series. This part also includes topics not normally covered in these texts, such as the extended and inverse autocorrelation function, the decomposition of the forecast function of ARIMA models, a detailed analysis of outliers and influential observations and automatic methods for model building and model based seasonal adjustment. For a basic course this book should be complemented with some of the excellent texts available. The book would be very well suited for an advanced course in which some of the basic material can be quickly reviewed using the first part, that skips many details and concentrates in the main concepts of general applicability. Then the course can concentrate in the topics in Parts 2 and 3. If the scope of the course is more in methodological extensions of univariate linear models the material in Part 2 can be useful, whereas if the objective is to introduce multivariate modeling Part 3 will be appropriate. To facilitate the use of the book as a text, all the time series data used in this book can be down loaded from the web address: http://gsb.uchicago.edu/ fac/ruey. tsay/teaching/ecas/

We are grateful to all people who have made this book possible: (1) to the 11 authors of the chapters of the book who have been extremely helpful in the timely revisions of the drafts of the chapters and have made a big effort to unify the presentation and (2) to the organizers of the course and all the students from many different countries in four continents that made this one week of lectures a very enjoyable experience for all the participants. We are very grateful to our host in the Monastery of El Escorial, father Agustin Alonso, who did his best to make our staying in the monastery an unforgettable experience. The success of the course was in large part due to the enthusiastic work in all the organization details of Ana Justel, Regina Kaiser, Juan Romo, Esther Ruiz, and Maria Jesus Sanchez. In the preparation of the book we are also grateful to Monica Benito for her help in organizing the index and the references in the book.

The Editors

About ECAS

ECAS is a foundation of Statistical Societies within Europe that, according to its constitution, was founded in order to foster links and to promote cooperation between statisticians in Europe. In order to achieve these aims, courses on an advanced level covering varying aspects of statistics are organized every 2 years in different countries of Europe. In 1999 Statistical Societies members of ECAS belongs to the following countries: Austria, Belgium, Denmark, France, Finland, Germany, Italy, Portugal, Spain, Sweden, Switzerland, The Netherlands, and the United Kingdom.

The first ECAS course was held in Capri, Italy, on Multidimensional Data Analysis in 1987. Subsequent courses were held on robustness in statistics in 1989 in the castle Reisenburg, Germany; on experimental design in 1991 in SW, France; on the analysis of categorical data in 1995 in Leiden, The Netherlands; on longitudinal data analysis and repeated measures in 1995 in Milton Keynes, United Kingdom; on time series analysis in 1997 in San Lorenzo del Escorial, Spain; and on environmental statistics in 1999 in Garpenberg, Sweden.

A Council has the overall responsibility for ECAS. Its members are nominated by the statistical societies of participating countries. The Presidents of ECAS have been Jean Jacques Debrosque (Belgium, 1987-1993) and Siegfried Heiler (Germany, 1994-1997). The current President is Daniel Pefia (Spain, 1998-2001).

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