Wiley Series in Probability and Statistics Series

Book 662

A self-contained, contemporary treatment of the analysis of long-range dependent data Long-Memory Time Series: Theory and Methods provides an overview of the theory and methods developed to deal with long-range dependent data and describes the applications of these methodologies to real-life time series. Systematically organized, it begins with the foundational essentials, proceeds to the analysis of methodological aspects (Estimation Methods, Asymptotic Theory, Heteroskedastic Models, Transformations, Bayesian Methods, and Prediction), and then extends these techniques to more complex data structures. To facilitate understanding, the book: * Assumes a basic knowledge of calculus and linear algebra and explains the more advanced statistical and mathematical concepts * Features numerous examples that accelerate understanding and illustrate various consequences of the theoretical results * Proves all theoretical results (theorems, lemmas, corollaries, etc.) or refers readers to resources with further demonstration * Includes detailed analyses of computational aspects related to the implementation of the methodologies described, including algorithm efficiency, arithmetic complexity, CPU times, and more * Includes proposed problems at the end of each chapter to help readers solidify their understanding and practice their skills A valuable real-world reference for researchers and practitioners in time series analysis, economerics, finance, and related fields, this book is also excellent for a beginning graduate-level course in long-memory processes or as a supplemental textbook for those studying advanced statistics, mathematics, economics, finance, engineering, or physics.
A companion Web site is available for readers to access the S-Plus and R data sets used within the text.

Multivariate Time Series Analysis

by Wilfredo Palma

Published 6 December 2013

Multivariate Time Series is the result of more than 20 years of teaching courses at both the beginning-graduate level. The main motivation is to provide a broad coverage of the most fundamental aspects of multivariate time series analysis and its applications at an appropriate (sometimes challenging) level. As a consequence, the author makes every attempt to strike a balance between clarity of exposition and mathematical rigor. A very detailed, but approachable overview of both VAR and VARMA models is carefully woven throughout the contents. The text provides an updated coverage of several useful and newly-developed techniques such as methods for analyzing financial time series, cointegration, time-varying models, Bayesian methods, portfolio analysis, and linear dynamical systems, among others. The topics are systematically organized in a progressive manner so as to provide suitable continuity from beginning to end. Examples, exercise sets, and their corresponding solutions are plentiful. Theory is discussed when relevant. To facilitate the reading, the book is self-contained. Apart from assuming some elementary knowledge of calculus and linear algebra, all the more advanced statistical and mathematical concepts used in a given chapter have been previously defined in the text. In the introductory chapter, a brief overview of multivariate random variables and matrices is provided for ease of transition from univariate to multivariate concepts. A companion Web site is available for readers to access the relevant (but limited) R data sets that are used within the text.

Time Series Analysis

by Wilfredo Palma

Published 18 October 2013

A modern and accessible guide to the analysis of introductory time series data Featuring an organized and self-contained guide, Time Series Analysis provides a broad introduction to the most fundamental methodologies and techniques of time series analysis. The book focuses on the treatment of univariate time series by illustrating a number of well-known models such as ARMA and ARIMA. Providing contemporary coverage, the book features several useful and newlydeveloped techniques such as weak and strong dependence, Bayesian methods, non-Gaussian data, local stationarity, missing values and outliers, and threshold models.
Time Series Analysis includes practical applications of time series methods throughout, as well as: * Real-world examples and exercise sets that allow readers to practice the presented methods and techniques * Numerous detailed analyses of computational aspects related to the implementation of methodologies including algorithm efficiency, arithmetic complexity, and process time * End-of-chapter proposed problems and bibliographical notes to deepen readers knowledge of the presented material * Appendices that contain details on fundamental concepts and select solutions of the problems implemented throughout * A companion website with additional data fi les and computer codes Time Series Analysis is an excellent textbook for undergraduate and beginning graduate-level courses in time series as well as a supplement for students in advanced statistics, mathematics, economics, finance, engineering, and physics. The book is also a useful reference for researchers and practitioners in time series analysis, econometrics, and finance. Wilfredo Palma, PhD, is Professor of Statistics in the Department of Statistics at Pontificia Universidad Catolica de Chile.
He has published several refereed articles and has received over a dozen academic honors and awards. His research interests include time series analysis, prediction theory, state space systems, linear models, and econometrics. He is the author of Long-Memory Time Series: Theory and Methods, also published by Wiley.