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Forecasting Structural Time Series Models The Kalman Filter


Forecasting Structural Time Series Models The Kalman Filter
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Forecasting Structural Time Series Models And The Kalman Filter


Forecasting Structural Time Series Models And The Kalman Filter
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Author : Andrew C. Harvey
language : en
Publisher: Cambridge University Press
Release Date : 1990

Forecasting Structural Time Series Models And The Kalman Filter written by Andrew C. Harvey 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 1990 with Business & Economics categories.


A synthesis of concepts and materials, that ordinarily appear separately in time series and econometrics literature, presents a comprehensive review of theoretical and applied concepts in modeling economic and social time series.



Forecasting Structural Time Series Models And The Kalman Filter


Forecasting Structural Time Series Models And The Kalman Filter
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Author : Andrew C. Harvey
language : en
Publisher: Cambridge University Press
Release Date : 1990-02-22

Forecasting Structural Time Series Models And The Kalman Filter written by Andrew C. Harvey 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 1990-02-22 with Business & Economics categories.


In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.



Time Series Models


Time Series Models
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Author : Andrew C. Harvey
language : en
Publisher: Financial Times/Prentice Hall
Release Date : 1993

Time Series Models written by Andrew C. Harvey and has been published by Financial Times/Prentice Hall this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with Time-series analysis categories.


A companion volume to The Econometric Analysis of Time series, this book focuses on the estimation, testing and specification of dynamic models which are not based on any behavioural theory. It covers univariate and multivariate time series and emphasizes autoregressive moving-average processes.



Forecasting Structural Time Series Models The Kalman Filter


Forecasting Structural Time Series Models The Kalman Filter
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Author : Andrew C. Harvey
language : en
Publisher:
Release Date : 2014-05-18

Forecasting Structural Time Series Models The Kalman Filter 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 2014-05-18 with Electronic books categories.


This book provides a synthesis of concepts and materials that ordinarily appear separately in time series and econometrics literature, presenting a comprehensive review of both theoretical and applied concepts. Perhaps the most novel feature of the book is its use of Kalman filtering together with econometric and time series methodology. From a technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. This technique was originally developed in control engineering but is becoming increasingly important in economics and operations research. The book is primarily concerned with modeling economic and social time series and with addressing the special problems that the treatment of such series pose.



Economic Time Series


Economic Time Series
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Author : William R. Bell
language : en
Publisher: CRC Press
Release Date : 2012-03-19

Economic Time Series written by William R. Bell and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-03-19 with Mathematics categories.


Economic Time Series: Modeling and Seasonality is a focused resource on analysis of economic time series as pertains to modeling and seasonality, presenting cutting-edge research that would otherwise be scattered throughout diverse peer-reviewed journals. This compilation of 21 chapters showcases the cross-fertilization between the fields of time series modeling and seasonal adjustment, as is reflected both in the contents of the chapters and in their authorship, with contributors coming from academia and government statistical agencies. For easier perusal and absorption, the contents have been grouped into seven topical sections: Section I deals with periodic modeling of time series, introducing, applying, and comparing various seasonally periodic models Section II examines the estimation of time series components when models for series are misspecified in some sense, and the broader implications this has for seasonal adjustment and business cycle estimation Section III examines the quantification of error in X-11 seasonal adjustments, with comparisons to error in model-based seasonal adjustments Section IV discusses some practical problems that arise in seasonal adjustment: developing asymmetric trend-cycle filters, dealing with both temporal and contemporaneous benchmark constraints, detecting trading-day effects in monthly and quarterly time series, and using diagnostics in conjunction with model-based seasonal adjustment Section V explores outlier detection and the modeling of time series containing extreme values, developing new procedures and extending previous work Section VI examines some alternative models and inference procedures for analysis of seasonal economic time series Section VII deals with aspects of modeling, estimation, and forecasting for nonseasonal economic time series By presenting new methodological developments as well as pertinent empirical analyses and reviews of established methods, the book provides much that is stimulating and practically useful for the serious researcher and analyst of economic time series.



Introduction And Implementations Of The Kalman Filter


Introduction And Implementations Of The Kalman Filter
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Author : Felix Govaers
language : en
Publisher: BoD – Books on Demand
Release Date : 2019-05-22

Introduction And Implementations Of The Kalman Filter written by Felix Govaers and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-22 with Computers categories.


Sensor data fusion is the process of combining error-prone, heterogeneous, incomplete, and ambiguous data to gather a higher level of situational awareness. In principle, all living creatures are fusing information from their complementary senses to coordinate their actions and to detect and localize danger. In sensor data fusion, this process is transferred to electronic systems, which rely on some "awareness" of what is happening in certain areas of interest. By means of probability theory and statistics, it is possible to model the relationship between the state space and the sensor data. The number of ingredients of the resulting Kalman filter is limited, but its applications are not.



Forecasting Principles And Practice


Forecasting Principles And Practice
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Author : Rob J Hyndman
language : en
Publisher: OTexts
Release Date : 2018-05-08

Forecasting Principles And Practice written by Rob J Hyndman and has been published by OTexts this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-08 with Business & Economics categories.


Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.



Introduction To Time Series Modeling


Introduction To Time Series Modeling
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Author : Genshiro Kitagawa
language : en
Publisher: CRC Press
Release Date : 2010-04-21

Introduction To Time Series Modeling written by Genshiro Kitagawa and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-04-21 with Mathematics categories.


In time series modeling, the behavior of a certain phenomenon is expressed in relation to the past values of itself and other covariates. Since many important phenomena in statistical analysis are actually time series and the identification of conditional distribution of the phenomenon is an essential part of the statistical modeling, it is very im



An Introduction To State Space Time Series Analysis


An Introduction To State Space Time Series Analysis
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Author : Jacques J. F. Commandeur
language : en
Publisher: OUP Oxford
Release Date : 2007-07-19

An Introduction To State Space Time Series Analysis written by Jacques J. F. Commandeur and has been published by OUP Oxford this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-07-19 with Business & Economics categories.


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 By State Space Methods


Time Series Analysis By State Space Methods
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Author : James Durbin
language : en
Publisher: Oxford University Press
Release Date : 2012-05-03

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 2012-05-03 with Business & Economics categories.


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.