Applied Nonlinear Time Series Analysis PDF Download
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Author: Michael Small
Publisher: World Scientific
ISBN: 981256117X
Category : Mathematics
Languages : en
Pages : 262
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Book Description
A collection of photographs focusing on the fading traditions, heritage and culture in County Cork Ireland.
Author: Michael Small
Publisher: World Scientific
ISBN: 981256117X
Category : Mathematics
Languages : en
Pages : 262
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Book Description
A collection of photographs focusing on the fading traditions, heritage and culture in County Cork Ireland.
Author: Ruey S. Tsay
Publisher: John Wiley & Sons
ISBN: 1119264073
Category : Mathematics
Languages : en
Pages : 512
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Book Description
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: Philip Hans Franses
Publisher: Cambridge University Press
ISBN: 0521770416
Category : Business & Economics
Languages : en
Pages : 299
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Book Description
This 2000 volume reviews non-linear time series models, and their applications to financial markets.
Author: RongHui Miao
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Jiti Gao
Publisher: CRC Press
ISBN: 1420011219
Category : Mathematics
Languages : en
Pages : 249
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Book Description
Useful in the theoretical and empirical analysis of nonlinear time series data, semiparametric methods have received extensive attention in the economics and statistics communities over the past twenty years. Recent studies show that semiparametric methods and models may be applied to solve dimensionality reduction problems arising from using fully
Author: Ray Huffaker
Publisher: Oxford University Press
ISBN: 0191085790
Category : Mathematics
Languages : en
Pages : 312
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Book Description
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.
Author: Jianqing Fan
Publisher: Springer Science & Business Media
ISBN: 0387693955
Category : Mathematics
Languages : en
Pages : 565
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Book Description
This is the first book that integrates useful parametric and nonparametric techniques with time series modeling and prediction, the two important goals of time series analysis. Such a book will benefit researchers and practitioners in various fields such as econometricians, meteorologists, biologists, among others who wish to learn useful time series methods within a short period of time. The book also intends to serve as a reference or text book for graduate students in statistics and econometrics.
Author: Ruey S. Tsay
Publisher: John Wiley & Sons
ISBN: 1118017099
Category : Mathematics
Languages : en
Pages : 724
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Book Description
This book provides a broad, mature, and systematic introduction to current financial econometric models and their applications to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout the book to apply the models and methods described. The author begins with basic characteristics of financial time series data before covering three main topics: Analysis and application of univariate financial time series The return series of multiple assets Bayesian inference in finance methods Key features of the new edition include additional coverage of modern day topics such as arbitrage, pair trading, realized volatility, and credit risk modeling; a smooth transition from S-Plus to R; and expanded empirical financial data sets. The overall objective of the book is to provide some knowledge of financial time series, introduce some statistical tools useful for analyzing these series and gain experience in financial applications of various econometric methods.
Author: Massimo Guidolin
Publisher: Academic Press
ISBN: 0128134100
Category : Business & Economics
Languages : en
Pages : 435
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Book Description
Essentials of Time Series for Financial Applications serves as an agile reference for upper level students and practitioners who desire a formal, easy-to-follow introduction to the most important time series methods applied in financial applications (pricing, asset management, quant strategies, and risk management). Real-life data and examples developed with EViews illustrate the links between the formal apparatus and the applications. The examples either directly exploit the tools that EViews makes available or use programs that by employing EViews implement specific topics or techniques. The book balances a formal framework with as few proofs as possible against many examples that support its central ideas. Boxes are used throughout to remind readers of technical aspects and definitions and to present examples in a compact fashion, with full details (workout files) available in an on-line appendix. The more advanced chapters provide discussion sections that refer to more advanced textbooks or detailed proofs. Provides practical, hands-on examples in time-series econometrics Presents a more application-oriented, less technical book on financial econometrics Offers rigorous coverage, including technical aspects and references for the proofs, despite being an introduction Features examples worked out in EViews (9 or higher)
Author: Philip Rothman
Publisher: Springer Science & Business Media
ISBN: 0792383796
Category : Business & Economics
Languages : en
Pages : 394
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Book Description
Nonlinear Time Series Analysis of Economic and Financial Data provides an examination of the flourishing interest that has developed in this area over the past decade. The constant theme throughout this work is that standard linear time series tools leave unexamined and unexploited economically significant features in frequently used data sets. The book comprises original contributions written by specialists in the field, and offers a combination of both applied and methodological papers. It will be useful to both seasoned veterans of nonlinear time series analysis and those searching for an informative panoramic look at front-line developments in the area.
