Download or read book Topics in Conditional Heteroscedastic Time Series Modelling written by Heung Wong and published by . This book was released on 1995 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Topics in Conditional Heteroscedastic Time Series Modelling written by 黃香 and published by . This book was released on 2017-01-27 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "Topics in Conditional Heteroscedastic Time Series Modelling" by 黃香, Heung, Wong, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. DOI: 10.5353/th_b3123451 Subjects: Autoregression (Statistics) Heteroscedasticity Time-series analysis
Download or read book Estimation in Conditionally Heteroscedastic Time Series Models written by Daniel Straumann and published by Springer Science & Business Media. This book was released on 2006-01-27 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: In his seminal 1982 paper, Robert F. Engle described a time series model with a time-varying volatility. Engle showed that this model, which he called ARCH (autoregressive conditionally heteroscedastic), is well-suited for the description of economic and financial price. Nowadays ARCH has been replaced by more general and more sophisticated models, such as GARCH (generalized autoregressive heteroscedastic). This monograph concentrates on mathematical statistical problems associated with fitting conditionally heteroscedastic time series models to data. This includes the classical statistical issues of consistency and limiting distribution of estimators. Particular attention is addressed to (quasi) maximum likelihood estimation and misspecified models, along to phenomena due to heavy-tailed innovations. The used methods are based on techniques applied to the analysis of stochastic recurrence equations. Proofs and arguments are given wherever possible in full mathematical rigour. Moreover, the theory is illustrated by examples and simulation studies.
Download or read book Applied Time Series Econometrics written by Helmut Lütkepohl and published by Cambridge University Press. This book was released on 2004-08-02 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: Time series econometrics is a rapidly evolving field. Particularly, the cointegration revolution has had a substantial impact on applied analysis. Hence, no textbook has managed to cover the full range of methods in current use and explain how to proceed in applied domains. This gap in the literature motivates the present volume. The methods are sketched out, reminding the reader of the ideas underlying them and giving sufficient background for empirical work. The treatment can also be used as a textbook for a course on applied time series econometrics. Topics include: unit root and cointegration analysis, structural vector autoregressions, conditional heteroskedasticity and nonlinear and nonparametric time series models. Crucial to empirical work is the software that is available for analysis. New methodology is typically only gradually incorporated into existing software packages. Therefore a flexible Java interface has been created, allowing readers to replicate the applications and conduct their own analyses.
Download or read book Stationary and Non Stationary Time Series Models with Conditional Heteroscedasticity written by 凌仕卿 and published by . This book was released on 2017-01-27 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "Stationary and Non-stationary Time Series Models With Conditional Heteroscedasticity" by 凌仕卿, Shiqing, Ling, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. DOI: 10.5353/th_b3123600 Subjects: Time-series analysis Heteroscedasticity
Download or read book Generalized Autoregressive Conditional Heteroscedastic Time Series Models written by Michael S. Lo and published by . This book was released on 2003 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Course in Time Series Analysis written by Daniel Peña and published by John Wiley & Sons. This book was released on 2011-01-25 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: New statistical methods and future directions of research in time series A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, and signal extraction. They then move on to advanced topics, focusing on heteroscedastic models, nonlinear time series models, Bayesian time series analysis, nonparametric time series analysis, and neural networks. Multivariate time series coverage includes presentations on vector ARMA models, cointegration, and multivariate linear systems. Special features include: Contributions from eleven of the worldâ??s leading figures in time series Shared balance between theory and application Exercise series sets Many real data examples Consistent style and clear, common notation in all contributions 60 helpful graphs and tables Requiring no previous knowledge of the subject, A Course in Time Series Analysis is an important reference and a highly useful resource for researchers and practitioners in statistics, economics, business, engineering, and environmental analysis. An Instructor's Manual presenting detailed solutions to all the problems in he book is available upon request from the Wiley editorial department.
Download or read book Time Series Models written by Manfred Deistler and published by Springer Nature. This book was released on 2022-10-21 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook provides a self-contained presentation of the theory and models of time series analysis. Putting an emphasis on weakly stationary processes and linear dynamic models, it describes the basic concepts, ideas, methods and results in a mathematically well-founded form and includes numerous examples and exercises. The first part presents the theory of weakly stationary processes in time and frequency domain, including prediction and filtering. The second part deals with multivariate AR, ARMA and state space models, which are the most important model classes for stationary processes, and addresses the structure of AR, ARMA and state space systems, Yule-Walker equations, factorization of rational spectral densities and Kalman filtering. Finally, there is a discussion of Granger causality, linear dynamic factor models and (G)ARCH models. The book provides a solid basis for advanced mathematics students and researchers in fields such as data-driven modeling, forecasting and filtering, which are important in statistics, control engineering, financial mathematics, econometrics and signal processing, among other subjects.
Download or read book A Test for Conditional Heteroskedasticity in Time Series Models written by Anil K. Bera and published by London : Department of Economics, University of Western Ontario. This book was released on 1990 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Time Series Models written by Andrew C. Harvey and published by . This book was released on 1981 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stationary stochastic process and their properties in the time domain; The frequency domain; State space models and the kalman filter; Estimation of autoregressive moving average models; Model building and prediction; Selected topics in time series regression.
Download or read book Time Series and Related Topics written by Ching-Zong Wei and published by IMS. This book was released on 2006 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Modeling Time Series with Conditional Heteroscedastic Structure written by Ratnayake Mudiyanselage Isuru Panduka Ratnayake and published by . This book was released on 2021 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Models with a conditional heteroscedastic variance structure play a vital role in many applications, including modeling financial volatility. In this dissertation several existing formulations, motivated by the Generalized Autoregressive Conditional Heteroscedastic model, are further generalized to provide more effective modeling of price range data well as count data. First, the Conditional Autoregressive Range (CARR) model is generalized by introducing a composite range-based multiplicative component formulation named the Composite CARR model. This formulation enables a more effective modeling of the long and short-term volatility components present in price range data. It treats the long-term volatility as a stochastic component that in itself exhibits conditional volatility. The Generalized Feedback Asymmetric CARR model presented in this dissertation is a generalization of the Feedback Asymmetric CARR model, with lagged cross-conditional range terms added to allow complete feedback across the two equations that model upward and downward price ranges. A regime-switching Threshold Asymmetric CARR model is also proposed. Its formulation captures both asymmetry and non-linearity, which are two main characteristics that exist in the price range data. This model handles asymmetry and non-linearity better than its range-based competitors, based on the Akaike's Information Criteria. In addition to the above models, a Time Varying Zero Inflated Poisson Integer GARCH model is introduced. This model enables the modeling of time series of count data with excess number of zeroes where this excess varies with time. In this model, the zero inflation component is modeled either as a deterministic function of time or as a vector of stochastic variables"--Abstract, page iv.
Download or read book Nonlinear Time Series Analysis written by Ruey S. Tsay and published by John Wiley & Sons. This book was released on 2018-09-14 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Tail Estimation and Conditional Modeling of Heteroscedastic Time Series written by Marc S. Paolella and published by . This book was released on 1999 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Time Series Analysis written by George E. P. Box and published by John Wiley & Sons. This book was released on 2015-06-02 with total page 712 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for the Fourth Edition "The book follows faithfully the style of the original edition. The approach is heavily motivated by real-world time series, and by developing a complete approach to model building, estimation, forecasting and control." —Mathematical Reviews Bridging classical models and modern topics, the Fifth Edition of Time Series Analysis: Forecasting and Control maintains a balanced presentation of the tools for modeling and analyzing time series. Also describing the latest developments that have occurred in the field over the past decade through applications from areas such as business, finance, and engineering, the Fifth Edition continues to serve as one of the most influential and prominent works on the subject. Time Series Analysis: Forecasting and Control, Fifth Edition provides a clearly written exploration of the key methods for building, classifying, testing, and analyzing stochastic models for time series and describes their use in five important areas of application: forecasting; determining the transfer function of a system; modeling the effects of intervention events; developing multivariate dynamic models; and designing simple control schemes. Along with these classical uses, the new edition covers modern topics with new features that include: A redesigned chapter on multivariate time series analysis with an expanded treatment of Vector Autoregressive, or VAR models, along with a discussion of the analytical tools needed for modeling vector time series An expanded chapter on special topics covering unit root testing, time-varying volatility models such as ARCH and GARCH, nonlinear time series models, and long memory models Numerous examples drawn from finance, economics, engineering, and other related fields The use of the publicly available R software for graphical illustrations and numerical calculations along with scripts that demonstrate the use of R for model building and forecasting Updates to literature references throughout and new end-of-chapter exercises Streamlined chapter introductions and revisions that update and enhance the exposition Time Series Analysis: Forecasting and Control, Fifth Edition is a valuable real-world reference for researchers and practitioners in time series analysis, econometrics, finance, and related fields. The book is also an excellent textbook for beginning graduate-level courses in advanced statistics, mathematics, economics, finance, engineering, and physics.
Download or read book Modeling Conditional Heteroskedasticity in Time Series and Spatial Analysis written by Pradosh Kumar Simlai and published by . This book was released on 2006 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: My dissertation consists of three separate chapters. In the first chapter I introduce semiparametric modeling of dependent data using estimating function approach. Once aim of this chapter is to provide an account of the developments relating to the theory of estimating functions. Starting from the simple case of a single parameter under independence, I cover the multiparameter, presence of nuisance parameters and dependent data cases.
Download or read book Stationary and Non stationary Time Series Models with Conditional Heteroscedasticity written by Shiqing Ling and published by . This book was released on 1997 with total page 600 pages. Available in PDF, EPUB and Kindle. Book excerpt:
