Threshold Models In Non Linear Time Series Analysis PDF Download
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Author | : H. Tong |
Publisher | : Springer Science & Business Media |
Total Pages | : 333 |
Release | : 2012-12-06 |
Genre | : Mathematics |
ISBN | : 1468478885 |
Download Threshold Models in Non-linear Time Series Analysis Book in PDF, ePub and Kindle
In the last two years or so, I was most fortunate in being given opportunities of lecturing on a new methodology to a variety of audiences in Britain, China, Finland, France and Spain. Despite my almost Confucian attitude of preferring talking (i.e. a transient record) to writing (i.e. a permanent record), the warm encouragement of friends has led to the ensuing notes. I am also only too conscious of the infancy of the methodology introduced in these notes. However, it is my sincere hope that exposure to a wider audience will accelerate its maturity. Readers are assumed to be familiar with the basic theory of time series analysis. The book by Professor M.B. Priestley (1981) may be used as a general reference. Chapter One is addressed to the general question: "why do we need non-linear time series models?" After describing some significant advantages of linear models, it singles out several major limitations of linearity. Of course, the selection reflects my personal view on the subject, which is only at its very beginning, although there does seem to be a general agreement in the literature that time irr'eversibility and limit cycles are among the most obvious.
Author | : H. Tong |
Publisher | : Springer |
Total Pages | : 348 |
Release | : 1983-11 |
Genre | : Gardening |
ISBN | : |
Download Threshold Models in Non-linear Time Series Analysis Book in PDF, ePub and Kindle
In the last two years or so, I was most fortunate in being given opportunities of lecturing on a new methodology to a variety of audiences in Britain, China, Finland, France and Spain. Despite my almost Confucian attitude of preferring talking (i.e. a transient record) to writing (i.e. a permanent record), the warm encouragement of friends has led to the ensuing notes. I am also only too conscious of the infancy of the methodology introduced in these notes. However, it is my sincere hope that exposure to a wider audience will accelerate its maturity. Readers are assumed to be familiar with the basic theory of time series analysis. The book by Professor M.B. Priestley (1981) may be used as a general reference. Chapter One is addressed to the general question: "why do we need non-linear time series models?" After describing some significant advantages of linear models, it singles out several major limitations of linearity. Of course, the selection reflects my personal view on the subject, which is only at its very beginning, although there does seem to be a general agreement in the literature that time irr'eversibility and limit cycles are among the most obvious.
Author | : H. Tong |
Publisher | : |
Total Pages | : 336 |
Release | : 1983-11-01 |
Genre | : |
ISBN | : 9781468478891 |
Download Threshold Models in Non-Linear Time Series Analysis Book in PDF, ePub and Kindle
Author | : Howell Tong |
Publisher | : |
Total Pages | : 323 |
Release | : 1983 |
Genre | : |
ISBN | : |
Download Threshold models in non-linear time series analysis Book in PDF, ePub and Kindle
Author | : Maurice Bertram Priestley |
Publisher | : |
Total Pages | : 258 |
Release | : 1988 |
Genre | : Mathematics |
ISBN | : |
Download Non-linear and Non-stationary Time Series Analysis Book in PDF, ePub and Kindle
Author | : Ruey S. Tsay |
Publisher | : John Wiley & Sons |
Total Pages | : 512 |
Release | : 2018-09-14 |
Genre | : Mathematics |
ISBN | : 1119264073 |
Download Nonlinear Time Series Analysis Book in PDF, ePub and Kindle
A comprehensive resource that draws a balance between theory and applications of nonlinear time series analysis Nonlinear Time Series Analysis offers an important guide to both parametric and nonparametric methods, nonlinear state-space models, and Bayesian as well as classical approaches to nonlinear time series analysis. The authors—noted experts in the field—explore the advantages and limitations of the nonlinear models and methods and review the improvements upon linear time series models. The need for this book is based on the recent developments in nonlinear time series analysis, statistical learning, dynamic systems and advanced computational methods. Parametric and nonparametric methods and nonlinear and non-Gaussian state space models provide a much wider range of tools for time series analysis. In addition, advances in computing and data collection have made available large data sets and high-frequency data. These new data make it not only feasible, but also necessary to take into consideration the nonlinearity embedded in most real-world time series. This vital guide: • Offers research developed by leading scholars of time series analysis • Presents R commands making it possible to reproduce all the analyses included in the text • Contains real-world examples throughout the book • Recommends exercises to test understanding of material presented • Includes an instructor solutions manual and companion website Written for students, researchers, and practitioners who are interested in exploring nonlinearity in time series, Nonlinear Time Series Analysis offers a comprehensive text that explores the advantages and limitations of the nonlinear models and methods and demonstrates the improvements upon linear time series models.
Author | : Randal Douc |
Publisher | : CRC Press |
Total Pages | : 548 |
Release | : 2014-01-06 |
Genre | : Mathematics |
ISBN | : 1466502347 |
Download Nonlinear Time Series Book in PDF, ePub and Kindle
This text emphasizes nonlinear models for a course in time series analysis. After introducing stochastic processes, Markov chains, Poisson processes, and ARMA models, the authors cover functional autoregressive, ARCH, threshold AR, and discrete time series models as well as several complementary approaches. They discuss the main limit theorems for Markov chains, useful inequalities, statistical techniques to infer model parameters, and GLMs. Moving on to HMM models, the book examines filtering and smoothing, parametric and nonparametric inference, advanced particle filtering, and numerical methods for inference.
Author | : Philip Hans Franses |
Publisher | : Cambridge University Press |
Total Pages | : 299 |
Release | : 2000-07-27 |
Genre | : Business & Economics |
ISBN | : 0521770416 |
Download Non-Linear Time Series Models in Empirical Finance Book in PDF, ePub and Kindle
This 2000 volume reviews non-linear time series models, and their applications to financial markets.
Author | : Howell Tong |
Publisher | : Oxford University Press, USA |
Total Pages | : 592 |
Release | : 1990 |
Genre | : Mathematics |
ISBN | : |
Download Non-linear Time Series Book in PDF, ePub and Kindle
Written by an internationally recognized expert in the field, this book provides a valuable introduction to the rapidly growing area of non-linear time series. Because developments in the study of dynamical systems have motivated many of the advances discussed here, the author's coverage includes such fundamental concepts of dynamical systems theory as limit cycles, Lyapunov functions, thresholds, and stability, with detailed descriptions of their role in the analysis of non-linear time series data. As the first accessible and comprehensive account of these exciting new developments, this unique volume bridges the gap between linear and chaotic time series analysis. Both statisticians and dynamical systems theorists will value its survey of recent developments and the present state of research, as well as the discussion of a number of unsolved problems in the field.
Author | : Ray Huffaker |
Publisher | : Oxford University Press |
Total Pages | : 312 |
Release | : 2017-10-20 |
Genre | : Mathematics |
ISBN | : 0191085790 |
Download Nonlinear Time Series Analysis with R Book in PDF, ePub and Kindle
Nonlinear Time Series Analysis with R provides a practical guide to emerging empirical techniques allowing practitioners to diagnose whether highly fluctuating and random appearing data are most likely driven by random or deterministic dynamic forces. It joins the chorus of voices recommending 'getting to know your data' as an essential preliminary evidentiary step in modelling. Time series are often highly fluctuating with a random appearance. Observed volatility is commonly attributed to exogenous random shocks to stable real-world systems. However, breakthroughs in nonlinear dynamics raise another possibility: highly complex dynamics can emerge endogenously from astoundingly parsimonious deterministic nonlinear models. Nonlinear Time Series Analysis (NLTS) is a collection of empirical tools designed to aid practitioners detect whether stochastic or deterministic dynamics most likely drive observed complexity. Practitioners become 'data detectives' accumulating hard empirical evidence supporting their modelling approach. This book is targeted to professionals and graduate students in engineering and the biophysical and social sciences. Its major objectives are to help non-mathematicians — with limited knowledge of nonlinear dynamics — to become operational in NLTS; and in this way to pave the way for NLTS to be adopted in the conventional empirical toolbox and core coursework of the targeted disciplines. Consistent with modern trends in university instruction, the book makes readers active learners with hands-on computer experiments in R code directing them through NLTS methods and helping them understand the underlying logic (please see www.marco.bittelli.com). The computer code is explained in detail so that readers can adjust it for use in their own work. The book also provides readers with an explicit framework — condensed from sound empirical practices recommended in the literature — that details a step-by-step procedure for applying NLTS in real-world data diagnostics.