Long-Memory Processes
Long-Memory Processes

Author: Jan Beran

Publisher: Springer Science & Business Media

Published: 2013-05-14

Total Pages: 892

ISBN-13: 3642355129

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Long-memory processes are known to play an important part in many areas of science and technology, including physics, geophysics, hydrology, telecommunications, economics, finance, climatology, and network engineering. In the last 20 years enormous progress has been made in understanding the probabilistic foundations and statistical principles of such processes. This book provides a timely and comprehensive review, including a thorough discussion of mathematical and probabilistic foundations and statistical methods, emphasizing their practical motivation and mathematical justification. Proofs of the main theorems are provided and data examples illustrate practical aspects. This book will be a valuable resource for researchers and graduate students in statistics, mathematics, econometrics and other quantitative areas, as well as for practitioners and applied researchers who need to analyze data in which long memory, power laws, self-similar scaling or fractal properties are relevant.

Statistics for Long-Memory Processes
Statistics for Long-Memory Processes

Author: Jan Beran

Publisher: CRC Press

Published: 1994-10-01

Total Pages: 336

ISBN-13: 9780412049019

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Statistical Methods for Long Term Memory Processes covers the diverse statistical methods and applications for data with long-range dependence. Presenting material that previously appeared only in journals, the author provides a concise and effective overview of probabilistic foundations, statistical methods, and applications. The material emphasizes basic principles and practical applications and provides an integrated perspective of both theory and practice. This book explores data sets from a wide range of disciplines, such as hydrology, climatology, telecommunications engineering, and high-precision physical measurement. The data sets are conveniently compiled in the index, and this allows readers to view statistical approaches in a practical context. Statistical Methods for Long Term Memory Processes also supplies S-PLUS programs for the major methods discussed. This feature allows the practitioner to apply long memory processes in daily data analysis. For newcomers to the area, the first three chapters provide the basic knowledge necessary for understanding the remainder of the material. To promote selective reading, the author presents the chapters independently. Combining essential methodologies with real-life applications, this outstanding volume is and indispensable reference for statisticians and scientists who analyze data with long-range dependence.

Time Series Analysis with Long Memory in View
Time Series Analysis with Long Memory in View

Author: Uwe Hassler

Publisher: John Wiley & Sons

Published: 2018-09-07

Total Pages: 361

ISBN-13: 1119470420

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Provides a simple exposition of the basic time series material, and insights into underlying technical aspects and methods of proof Long memory time series are characterized by a strong dependence between distant events. This book introduces readers to the theory and foundations of univariate time series analysis with a focus on long memory and fractional integration, which are embedded into the general framework. It presents the general theory of time series, including some issues that are not treated in other books on time series, such as ergodicity, persistence versus memory, asymptotic properties of the periodogram, and Whittle estimation. Further chapters address the general functional central limit theory, parametric and semiparametric estimation of the long memory parameter, and locally optimal tests. Intuitive and easy to read, Time Series Analysis with Long Memory in View offers chapters that cover: Stationary Processes; Moving Averages and Linear Processes; Frequency Domain Analysis; Differencing and Integration; Fractionally Integrated Processes; Sample Means; Parametric Estimators; Semiparametric Estimators; and Testing. It also discusses further topics. This book: Offers beginning-of-chapter examples as well as end-of-chapter technical arguments and proofs Contains many new results on long memory processes which have not appeared in previous and existing textbooks Takes a basic mathematics (Calculus) approach to the topic of time series analysis with long memory Contains 25 illustrative figures as well as lists of notations and acronyms Time Series Analysis with Long Memory in View is an ideal text for first year PhD students, researchers, and practitioners in statistics, econometrics, and any application area that uses time series over a long period. It would also benefit researchers, undergraduates, and practitioners in those areas who require a rigorous introduction to time series analysis.

Long Memory in Economics
Long Memory in Economics

Author: Gilles Teyssière

Publisher: Springer Science & Business Media

Published: 2006-09-22

Total Pages: 394

ISBN-13: 3540346252

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Assembles three different strands of long memory analysis: statistical literature on the properties of, and tests for, LRD processes; mathematical literature on the stochastic processes involved; and models from economic theory providing plausible micro foundations for the occurrence of long memory in economics.

Time Series with Long Memory
Time Series with Long Memory

Author: Peter M. Robinson

Publisher: Advanced Texts in Econometrics

Published: 2003

Total Pages: 396

ISBN-13: 9780199257300

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Long memory time series are characterized by a strong dependence between distant events.

Superior Memory
Superior Memory

Author: Elizabeth Valentine

Publisher: Psychology Press

Published: 2013-06-20

Total Pages: 192

ISBN-13: 1134836015

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This book examines the nature and causal antecedents of superior memory performance. The main theme is that such performance may depend on either specific memory techniques or natural superiority in the efficiency of one or more memory processes. Chapter 2 surveys current views about the structure of memory and discusses whether common processes can be identified which might underlie general variation in memory ability, or whether distinct memory subsystems exist, the efficiency of which varies independently of each other. Chapter 3 provides a comprehensive survey of existing evidence on superior memory performance. It examines techniques which underlie many examples of unusual memory performance, and concludes that not all this evidence is explicable in terms of such techniques. Relations between memory ability and other cognitive processes are also discussed. The remainder of the book describes the authors' own studies of a dozen memory experts, employing a wide variety of short- and long-term memory tasks. These studies provide a much larger body of data than previously available from studies of single individuals, usually restricted to a narrow range of tasks and rarely involving any systematic study of long-term retention. The authors argue that in some cases unusual memory ability is not dependent on the use of special techniques. They develop some objective criteria for distinguishing between subjects who demonstrate "natural" superiority and those "strategists" who depend on techniques. Natural superiority was characterised by superior performance on a wider range of tasks and better long-term retention. The existence of a general memory ability was further supported by a factor analysis of data from all subjects, omitting those who described highly-practised techniques. This analysis also demonstrated the independence of initial encoding and retention processes. The monograph raises many interesting questions concerning the existence and nature of individual differences in memory ability (a previously neglected topic), their relation to other cognitive processes and implications for theories concerning the structure of memory.

Modeling Financial Time Series with S-PLUS
Modeling Financial Time Series with S-PLUS

Author: Eric Zivot

Publisher: Springer Science & Business Media

Published: 2013-11-11

Total Pages: 632

ISBN-13: 0387217630

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The field of financial econometrics has exploded over the last decade 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. This 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 Second Edition is updated to cover S+FinMetrics 2.0 and includes new chapters on copulas, nonlinear regime switching models, continuous-time financial models, generalized method of moments, semi-nonparametric conditional density models, and the efficient method of moments. Eric Zivot is an associate professor and Gary Waterman Distinguished Scholar in the Economics Department, and adjunct associate professor of finance in the Business School at the University of Washington. He regularly teaches courses on econometric theory, financial econometrics and time series econometrics, and is the recipient of the Henry T. Buechel Award for Outstanding Teaching. He is an associate editor of Studies in Nonlinear Dynamics and Econometrics. He has published papers in the leading econometrics journals, including Econometrica, Econometric Theory, the Journal of Business and Economic Statistics, Journal of Econometrics, and the Review of Economics and Statistics. Jiahui Wang is an employee of Ronin Capital LLC. He received a Ph.D. in Economics from the University of Washington in 1997. He has published in leading econometrics journals such as Econometrica and Journal of Business and Economic Statistics, and is the Principal Investigator of National Science Foundation SBIR grants. In 2002 Dr. Wang was selected as one of the "2000 Outstanding Scholars of the 21st Century" by International Biographical Centre.

Heavy-Tailed Time Series
Heavy-Tailed Time Series

Author: Rafal Kulik

Publisher: Springer Nature

Published: 2020-07-01

Total Pages: 677

ISBN-13: 1071607375

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This book aims to present a comprehensive, self-contained, and concise overview of extreme value theory for time series, incorporating the latest research trends alongside classical methodology. Appropriate for graduate coursework or professional reference, the book requires a background in extreme value theory for i.i.d. data and basics of time series. Following a brief review of foundational concepts, it progresses linearly through topics in limit theorems and time series models while including historical insights at each chapter’s conclusion. Additionally, the book incorporates complete proofs and exercises with solutions as well as substantive reference lists and appendices, featuring a novel commentary on the theory of vague convergence.

Stochastic Processes and Long Range Dependence
Stochastic Processes and Long Range Dependence

Author: Gennady Samorodnitsky

Publisher: Springer

Published: 2016-11-09

Total Pages: 419

ISBN-13: 3319455753

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This monograph is a gateway for researchers and graduate students to explore the profound, yet subtle, world of long-range dependence (also known as long memory). The text is organized around the probabilistic properties of stationary processes that are important for determining the presence or absence of long memory. The first few chapters serve as an overview of the general theory of stochastic processes which gives the reader sufficient background, language, and models for the subsequent discussion of long memory. The later chapters devoted to long memory begin with an introduction to the subject along with a brief history of its development, followed by a presentation of what is currently the best known approach, applicable to stationary processes with a finite second moment. The book concludes with a chapter devoted to the author’s own, less standard, point of view of long memory as a phase transition, and even includes some novel results. Most of the material in the book has not previously been published in a single self-contained volume, and can be used for a one- or two-semester graduate topics course. It is complete with helpful exercises and an appendix which describes a number of notions and results belonging to the topics used frequently throughout the book, such as topological groups and an overview of the Karamata theorems on regularly varying functions.