Multivariate Time Series Analysis and Applications E-Book

William W. S. Wei

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: Wiley Series in Probability and Statistics
• Sprache: Englisch

Seitenzahl: 628

Veröffentlichungsjahr: 2018

Beschreibung

An essential guide on high dimensional multivariate time series including all the latest topics from one of the leading experts in the field

Following the highly successful and much lauded book, Time Series Analysis—Univariate and Multivariate Methods, this new work by William W.S. Wei focuses on high dimensional multivariate time series, and is illustrated with numerous high dimensional empirical time series. Beginning with the fundamentalconcepts and issues of multivariate time series analysis,this book covers many topics that are not found in general multivariate time series books. Some of these are repeated measurements, space-time series modelling, and dimension reduction. The book also looks at vector time series models, multivariate time series regression models, and principle component analysis of multivariate time series. Additionally, it provides readers with information on factor analysis of multivariate time series, multivariate GARCH models, and multivariate spectral analysis of time series.

With the development of computers and the internet, we have increased potential for data exploration. In the next few years, dimension will become a more serious problem. Multivariate Time Series Analysis and its Applications provides some initial solutions, which may encourage the development of related software needed for the high dimensional multivariate time series analysis.

• Written by bestselling author and leading expert in the field
• Covers topics not yet explored in current multivariate books
• Features classroom tested material
• Written specifically for time series courses

Multivariate Time Series Analysis and its Applications is designed for an advanced time series analysis course. It is a must-have for anyone studying time series analysis and is also relevant for students in economics, biostatistics, and engineering. 

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Multivariate Time Series Analysis and Applications

William W.S. Wei

Department of Statistical ScienceTemple University, Philadelphia, PA, USA

This edition first published 2019© 2019 John Wiley & Sons Ltd

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Library of Congress Cataloging‐in‐Publication data applied for

Hardback: 9781119502852

Cover design: WileyCover image: Courtesy of William W.S. Wei

To my Grandpa, Parents, Wife, Sons,and Daughter

About the author

William W.S. Wei is a Professor of Statistical Science at Temple University in Philadelphia, Pennsylvania, United States of America. He earned his BA in Economics from the National Taiwan University (1966), BA in Mathematics from the University of Oregon (1969), and M.S. (1972) and Ph.D. (1974) in Statistics from the University of Wisconsin, Madison. From 1982 to 1987, he was the Chair of the Department of Statistics at Temple University. He has been a Visiting Professor at many universities including the Nankai University in China, National University of Colombia in Colombia, Korea University in Korea, National Sun Yat‐Sen University, National Chiao Tung University, and National Taiwan University in Taiwan, and Middle East Technical University in Turkey. His research interests include time series analysis, forecasting methods, high dimensional problems, statistical modeling, and their applications. He has developed new methodologies in seasonal adjustment, aggregation and disaggregation, outlier detection, robust estimation, and vector time series analysis. Some of his most significant contributions include extensive research on the effects of aggregation, methods of measuring information loss due to aggregation, new stochastic procedures of performing data disaggregation, model‐free outlier detection techniques, robust methods of estimating autocorrelations, statistics for analyzing multivariate time series, and dimension reduction for high‐dimensional time series. His first book, Time Series Analysis – Univariate and Multivariate Methods, the first edition published in 1990 and the second edition published in 2006, has been translated into several languages and heavily cited by researchers worldwide. He has just completed his second book, Multivariate Time Series Analysis and Applications. He is an active educator and researcher. He has successfully supervised many Ph.D. students, who hold teaching positions at universities or leadership positions in government and industry throughout the world. He is a Fellow of the American Statistical Association, a Fellow of the Royal Statistical Society, and an Elected Member of the International Statistical Institute. He was the 2002 President of ICSA (International Chinese Statistical Association). He is currently an Associate Editor of the Journal of Forecasting and the Journal of Applied Statistical Science. In addition to teaching and research, he is also active in community service. He served on the educational advisory committee of his local school district, as the chair of the selection committee for a community high school scholarship program, and as the president of several community organizations, including the Taiwanese Hakka Associations of America. Among the many awards he has received are the 2014 Lifetime Achievement Award and the 2016 Musser Award for Excellence in Research from the Temple University Fox School of Business.

Preface

My main research area is time series analysis and forecasting. I have written a book, Time Series Analysis – Univariate and Multivariate Methods. Since the first edition was published in 1990 and the second edition in 2006, the book has been used by many researchers and universities worldwide, and I have received many encouraging letters and emails from researchers, instructors, and students about the usefulness of the book in their research and studies. It has been translated into several languages including Chinese, Spanish, and Portuguese. With the development of computers and the internet, we have had a data explosion, and many new theories and methods have been developed in high‐dimensional time series analysis. Many publishers have contacted me asking for a new edition. Because of the development of so much new material, it would be impractical to include it all in a new edition of the book. Therefore, I decided to write a new book and call it Multivariate Time Series Analysis and Applications. Due to the enthusiasm of multiple publishing editors, I had a difficult time deciding which publisher to choose. After consulting with Dr. Sarkar, our department chair, I decided to choose Wiley. After four stages of rigorous reviews, the Wiley directors unanimously approved the publication of this book.

Many research studies involve multivariate time series. For example, a study of monthly cancer rates in the United States during the past 10 years can involve 50, many hundreds, or even thousands of time series depending on whether we investigate the cancer rates for states, cities, or counties, and a study of the quarterly sales performance of one company's different branches in the country or the world from 2010 to 2018 may involve many hundreds or thousands of time series, depending on the number of products and the number of branches within the company. Multivariate time series analysis methods are needed to properly analyze the data in these studies, which are different from standard statistical theory and methods based on random samples that assume independence. Dependence is the fundamental nature of time series. The use of highly correlated high‐dimensional time series data introduces many complications and challenges. The methods and theory to solve these issues will make up the contents of this book. After introducing the fundamental concepts and reviewing the standard vector time series models, we will explore many important issues, including multivariate time series regression, dimension reduction and model simplification, multivariate GARCH (generalized autoregressive conditional heteroskedasticity) models, repeated measurement phenomenon, space–time series modeling, multivariate spectral analysis for both stationary and nonstationary vector time series, and the high‐dimension problem in multivariate time series analysis.

In this book, I follow the same fundamental themes of my research with a balanced emphasis on both theory and applications. Methodologies are introduced with proper theoretical justifications and illustrated with simulated and empirical data sets. It should be pointed out that this book is designed for a research‐oriented second time series analysis course and therefore standard exercises normally found in the first course will not be provided. Instead, research‐oriented projects will be suggested.

I am grateful to the Department of Statistical Science, Fox School of Business, and Temple University for granting me a sabbatical research leave to finish this book. Fox School is celebrating its 100‐year anniversary in 2018, and this book is my way of joining in its centennial celebration. I am also grateful to my wife, Susanna, a professor at Saint Joseph's University, sons and daughter, Stephen, Stanley, and Jessica, who are all married physicians, for their help in proofreading my manuscripts even as they are so busy with their own careers and families. My very sincere thanks go to Zeda Li, Andrew Gehman, Nandi Shinjini, and Kaijun Wang, who are either my Ph.D. dissertation students, research/teaching assistants, or both, and Kevin Liu, who was one of our excellent Masters students in the department, for their outstanding assistance in collecting data sets and developing software programs used in the book. Finally, I would like to thank Ms. Alison Oliver, Publications Manager, and Ms. Jemima Kingsly, Project Editor; Ms. Francesca McMahon, Editorial Assistant, Ms. Blesy Regulas, Project Editor; Mr. Mustaq Ahamed Noorullah, Production Editor, all at John Wiley & Sons, and Ms. Lynette Woodward, Editorial & Academic Services, UK, who have been invaluable in completing this important book project.

William W.S. WeiMarch 2018

About the companion website

This book is accompanied by a companion website:

www.wiley.com/go/wei/datasets

The website includes:

‐ Data Appendix (Bookdata)

Scan this QR code to visit the companion website.