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Time Series Analysis By State Space Methods


State Space Methods For Time Series Analysis
Author: James Durbin
Publisher: OUP Oxford
ISBN: 0191627194
Size: 28.38 MB
Format: PDF, ePub
View: 4593
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This new edition updates Durbin & Koopman's important text on the state space approach to time series analysis. The distinguishing feature of state space time series models is that observations are regarded as made up of distinct components such as trend, seasonal, regression elements and disturbance terms, each of which is modelled separately. The techniques that emerge from this approach are very flexible and are capable of handling a much wider range of problems than the main analytical system currently in use for time series analysis, the Box-Jenkins ARIMA system. Additions to this second edition include the filtering of nonlinear and non-Gaussian series. Part I of the book obtains the mean and variance of the state, of a variable intended to measure the effect of an interaction and of regression coefficients, in terms of the observations. Part II extends the treatment to nonlinear and non-normal models. For these, analytical solutions are not available so methods are based on simulation.




Time Series Analysis By State Space Methods


State Space Methods For Time Series Analysis
Author: James Durbin
Publisher: Oxford University Press
ISBN: 019964117X
Size: 70.84 MB
Format: PDF, ePub
View: 5950
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This is a comprehensive treatment of the state space approach to time series analysis. A distinguishing feature of state space time series models is that observations are regarded as made up of distinct components, which are each modelled separately.




An Introduction To State Space Time Series Analysis


State Space Methods For Time Series Analysis
Author: Jacques J. F. Commandeur
Publisher: OUP Oxford
ISBN: 0191607800
Size: 70.38 MB
Format: PDF, Kindle
View: 4729
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Providing a practical introduction to state space methods as applied to unobserved components time series models, also known as structural time series models, this book introduces time series analysis using state space methodology to readers who are neither familiar with time series analysis, nor with state space methods. The only background required in order to understand the material presented in the book is a basic knowledge of classical linear regression models, of which a brief review is provided to refresh the reader's knowledge. Also, a few sections assume familiarity with matrix algebra, however, these sections may be skipped without losing the flow of the exposition. The book offers a step by step approach to the analysis of the salient features in time series such as the trend, seasonal, and irregular components. Practical problems such as forecasting and missing values are treated in some detail. This useful book will appeal to practitioners and researchers who use time series on a daily basis in areas such as the social sciences, quantitative history, biology and medicine. It also serves as an accompanying textbook for a basic time series course in econometrics and statistics, typically at an advanced undergraduate level or graduate level.




Time Series Analysis For The State Space Model With R Stan


State Space Methods For Time Series Analysis
Author: Junichiro Hagiwara
Publisher: Springer
ISBN: 9789811607103
Size: 38.86 MB
Format: PDF, ePub
View: 4288
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This book provides a comprehensive and concrete illustration of time series analysis focusing on the state-space model, which has recently attracted increasing attention in a broad range of fields. The major feature of the book lies in its consistent Bayesian treatment regarding whole combinations of batch and sequential solutions for linear Gaussian and general state-space models: MCMC and Kalman/particle filter. The reader is given insight on flexible modeling in modern time series analysis. The main topics of the book deal with the state-space model, covering extensively, from introductory and exploratory methods to the latest advanced topics such as real-time structural change detection. Additionally, a practical exercise using R/Stan based on real data promotes understanding and enhances the reader’s analytical capability.




Multivariate Time Series With Linear State Space Structure


State Space Methods For Time Series Analysis
Author: Víctor Gómez
Publisher: Springer
ISBN: 3319285998
Size: 20.25 MB
Format: PDF
View: 6286
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This book presents a comprehensive study of multivariate time series with linear state space structure. The emphasis is put on both the clarity of the theoretical concepts and on efficient algorithms for implementing the theory. In particular, it investigates the relationship between VARMA and state space models, including canonical forms. It also highlights the relationship between Wiener-Kolmogorov and Kalman filtering both with an infinite and a finite sample. The strength of the book also lies in the numerous algorithms included for state space models that take advantage of the recursive nature of the models. Many of these algorithms can be made robust, fast, reliable and efficient. The book is accompanied by a MATLAB package called SSMMATLAB and a webpage presenting implemented algorithms with many examples and case studies. Though it lays a solid theoretical foundation, the book also focuses on practical application, and includes exercises in each chapter. It is intended for researchers and students working with linear state space models, and who are familiar with linear algebra and possess some knowledge of statistics.



Time Series Analysis by State Space Methods
Language: en
Pages: 368
Authors: James Durbin, Siem Jan Koopman
Categories: Business & Economics
Type: BOOK - Published: 2012-05-03 - Publisher: OUP Oxford
This new edition updates Durbin & Koopman's important text on the state space approach to time series analysis. The distinguishing feature of state space time series models is that observations are regarded as made up of distinct components such as trend, seasonal, regression elements and disturbance terms, each of which
Time Series Analysis by State Space Methods
Language: en
Pages: 346
Authors: James Durbin, Siem Jan Koopman
Categories: Business & Economics
Type: BOOK - Published: 2012-05-03 - Publisher: Oxford University Press
This is a comprehensive treatment of the state space approach to time series analysis. A distinguishing feature of state space time series models is that observations are regarded as made up of distinct components, which are each modelled separately.
An Introduction to State Space Time Series Analysis
Language: en
Pages: 192
Authors: Jacques J. F. Commandeur, Siem Jan Koopman
Categories: Business & Economics
Type: BOOK - Published: 2007-07-19 - Publisher: OUP Oxford
Providing a practical introduction to state space methods as applied to unobserved components time series models, also known as structural time series models, this book introduces time series analysis using state space methodology to readers who are neither familiar with time series analysis, nor with state space methods. The only
State-Space Methods for Time Series Analysis
Language: en
Pages: 270
Authors: Jose Casals, Alfredo Garcia-Hiernaux, Miguel Jerez, Sonia Sotoca, A. Alexandre Trindade
Categories: Mathematics
Type: BOOK - Published: 2018-09-03 - Publisher: CRC Press
The state-space approach provides a formal framework where any result or procedure developed for a basic model can be seamlessly applied to a standard formulation written in state-space form. Moreover, it can accommodate with a reasonable effort nonstandard situations, such as observation errors, aggregation constraints, or missing in-sample values. Exploring
Econometric With Matlab
Language: en
Pages: 282
Authors: A. Smith
Categories: Mathematics
Type: BOOK - Published: 2017-11-10 - Publisher: Createspace Independent Publishing Platform
Econometrics Toolbox provides functions for modeling economic data. You can select and estimate economic models for simulation and forecasting. For time series modeling and analysis, the toolbox includes univariate Bayesian linear regression, univariate ARIMAX/GARCH composite models with several GARCH variants, multivariate VARX models, and cointegration analysis. It also provides methods