State Space And Unobserved Component Models
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State Space And Unobserved Component Models
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Author : James Durbin
language : en
Publisher: Cambridge University Press
Release Date : 2004-06-10
State Space And Unobserved Component Models written by James Durbin and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-06-10 with Business & Economics categories.
A comprehensive overview of developments in the theory and application of state space modeling, first published in 2004.
State Space And Unobserved Component Models
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Author : Andrew C. Harvey
language : en
Publisher:
Release Date : 2004
State Space And Unobserved Component Models written by Andrew C. Harvey and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with State-space methods categories.
State Space And Unobserved Component Models
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Author : Andrew Harvey
language : en
Publisher:
Release Date : 2004
State Space And Unobserved Component Models written by Andrew Harvey and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.
State Space Models
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Author : Yong Zeng
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-08-15
State Space Models written by Yong Zeng and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-08-15 with Business & Economics categories.
State-space models as an important mathematical tool has been widely used in many different fields. This edited collection explores recent theoretical developments of the models and their applications in economics and finance. The book includes nonlinear and non-Gaussian time series models, regime-switching and hidden Markov models, continuous- or discrete-time state processes, and models of equally-spaced or irregularly-spaced (discrete or continuous) observations. The contributed chapters are divided into four parts. The first part is on Particle Filtering and Parameter Learning in Nonlinear State-Space Models. The second part focuses on the application of Linear State-Space Models in Macroeconomics and Finance. The third part deals with Hidden Markov Models, Regime Switching and Mathematical Finance and the fourth part is on Nonlinear State-Space Models for High Frequency Financial Data. The book will appeal to graduate students and researchers studying state-space modeling in economics, statistics, and mathematics, as well as to finance professionals.
Readings In Unobserved Components Models
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Author : Andrew Harvey
language : en
Publisher: OUP Oxford
Release Date : 2005-04-07
Readings In Unobserved Components Models written by Andrew Harvey and has been published by OUP Oxford this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-04-07 with Business & Economics categories.
This volume presents a collection of readings which give the reader an idea of the nature and scope of unobserved components (UC) models and the methods used to deal with them. The book is intended to give a self-contained presentation of the methods and applicative issues. Harvey has made major contributions to this field and provides substantial introductions throughout the book to form a unified view of the literature. - ;This book presents a collection of readings which give the reader an idea of the nature and scope of unobserved components (UC) models and the methods used to deal with them. It contains four parts, three of which concern recent theoretical developments in classical and Bayesian estimation of linear, nonlinear, and non Gaussian UC models, signal extraction and testing, and one is devoted to selected econometric applications. The first part focuses on the linear state space model; the readings provide insight on prediction theory, signal extraction, and likelihood inference for non stationary and non invertible processes, diagnostic checking, and the use of state space methods for spline smoothing. Part II deals with applications of linear UC models to various estimation problems concerning economic time series, such as trend-cycle decompositions, seasonal adjustment, and the modelling of the serial correlation induced by survey sample design. The issues involved in testing in linear UC models are the theme of part III, which considers tests concerned with whether or not certain variance parameters are zero, with special reference to stationarity tests. Finally, part IV is devoted to the advances concerning classical and Bayesian inference for non linear and non Gaussian state space models, an area that has been evolving very rapidly during the last decade, paralleling the advances in computational inference using stochastic simulation techniques. The book is intended to give a relatively self-contained presentation of the methods and applicative issues. For this purpose, each part comes with an introductory chapter by the editors that provides a unified view of the literature and the many important developments that have occurred in the last years. -
Time Series Modelling With Unobserved Components
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Author : Matteo M. Pelagatti
language : en
Publisher: CRC Press
Release Date : 2021-06-30
Time Series Modelling With Unobserved Components written by Matteo M. Pelagatti and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-30 with categories.
This work focuses on the unobserved components model (UCM) approach rather than general state space modeling. It provides enough theory so that readers understand the underlying mechanisms while keeping the mathematical rigor to a minimum.
Filtering None Linear State Space Models Methods And Economic Applications
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Author : Kai Ming Lee
language : en
Publisher: Rozenberg Publishers
Release Date : 2010
Filtering None Linear State Space Models Methods And Economic Applications written by Kai Ming Lee and has been published by Rozenberg Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
Time Series Analysis By State Space Methods
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Author : James Durbin
language : en
Publisher: Oxford University Press
Release Date : 2001-06-21
Time Series Analysis By State Space Methods written by James Durbin and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-06-21 with Business & Economics categories.
State space time series analysis emerged in the 1960s in engineering, but its applications have spread to other fields. Durbin (statistics, London School of Economics and Political Science) and Koopman (econometrics, Free U., Amsterdam) extol the virtues of such models over the main analytical system currently used for time series data, Box-Jenkins' ARIMA. What distinguishes state space time models is that they separately model components such as trend, seasonal, regression elements and disturbance terms. Part I focuses on traditional and new techniques based on the linear Gaussian model. Part II presents new material extending the state space model to non-Gaussian observations. c. Book News Inc.
Bayesian Inference Of State Space Models
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Author : Kostas Triantafyllopoulos
language : en
Publisher: Springer Nature
Release Date : 2021-11-12
Bayesian Inference Of State Space Models written by Kostas Triantafyllopoulos and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-12 with Mathematics categories.
Bayesian Inference of State Space Models: Kalman Filtering and Beyond offers a comprehensive introduction to Bayesian estimation and forecasting for state space models. The celebrated Kalman filter, with its numerous extensions, takes centre stage in the book. Univariate and multivariate models, linear Gaussian, non-linear and non-Gaussian models are discussed with applications to signal processing, environmetrics, economics and systems engineering. Over the past years there has been a growing literature on Bayesian inference of state space models, focusing on multivariate models as well as on non-linear and non-Gaussian models. The availability of time series data in many fields of science and industry on the one hand, and the development of low-cost computational capabilities on the other, have resulted in a wealth of statistical methods aimed at parameter estimation and forecasting. This book brings together many of these methods, presenting an accessible and comprehensive introduction to state space models. A number of data sets from different disciplines are used to illustrate the methods and show how they are applied in practice. The R package BTSA, created for the book, includes many of the algorithms and examples presented. The book is essentially self-contained and includes a chapter summarising the prerequisites in undergraduate linear algebra, probability and statistics. An up-to-date and complete account of state space methods, illustrated by real-life data sets and R code, this textbook will appeal to a wide range of students and scientists, notably in the disciplines of statistics, systems engineering, signal processing, data science, finance and econometrics. With numerous exercises in each chapter, and prerequisite knowledge conveniently recalled, it is suitable for upper undergraduate and graduate courses.
Applications Of State Space Models In Finance
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Author : Sascha Mergner
language : en
Publisher: Universitätsverlag Göttingen
Release Date : 2009
Applications Of State Space Models In Finance written by Sascha Mergner and has been published by Universitätsverlag Göttingen this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.
State space models play a key role in the estimation of time-varying sensitivities in financial markets. The objective of this book is to analyze the relative merits of modern time series techniques, such as Markov regime switching and the Kalman filter, to model structural changes in the context of widely used concepts in finance. The presented material will be useful for financial economists and practitioners who are interested in taking time-variation in the relationship between financial assets and key economic factors explicitly into account. The empirical part illustrates the application of the various methods under consideration. As a distinctive feature, it includes a comprehensive analysis of the ability of time-varying coefficient models to estimate and predict the conditional nature of systematic risks for European industry portfolios.