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Forecasting, Structural Time Series Models and the Kalman Filter by Andrew C. Harvey (1991, Trade Paperback)
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About this product
Product Identifiers
PublisherCambridge University Press
ISBN-100521405734
ISBN-139780521405737
eBay Product ID (ePID)317297
Product Key Features
Number of Pages572 Pages
Publication NameForecasting, Structural Time Series Models and the Kalman Filter
LanguageEnglish
SubjectEconometrics, Forecasting, Statistics
Publication Year1991
TypeTextbook
AuthorAndrew C. Harvey
Subject AreaBusiness & Economics
FormatTrade Paperback
Dimensions
Item Height1.3 in
Item Weight29.3 Oz
Item Length9 in
Item Width6 in
Additional Product Features
Intended AudienceScholarly & Professional
Reviews'... if you're looking for a state of the art monograph on applied aspects of state-space representations, and Kalman filtering ... then Harvey's book is required reading.' Econometric Theory, '... if you're looking for a state of the art monograph on applied aspects of state-space representations, and Kalman filtering ... then Harvey's book is required reading.'Econometric Theory, '… if you're looking for a state of the art monograph on applied aspects of state-space representations, and Kalman filtering … then Harvey's book is required reading.' Econometric Theory, "A well-written book by an author who has made numerous important contributions to the literature of forecasting, time series, and Kalman filters. It is a practical book in the sense that it not only discusses the definitions, interpretations, and analyses of structural time series models, but also illustrates the techniques." Choice, ‘… if you’re looking for a state of the art monograph on applied aspects of state-space representations, and Kalman filtering … then Harvey’s book is required reading.’Econometric Theory, "It is difficult to compare this well-written, practical book to other books on time series because it is unique and unconventional in its approach to the subject....It accomplishes the difficult task of making the subject accessible to students and practitioners having relatively modest preparation in mathematics and statistics. I recommend it for acquisition by any undergraduate/graduate sciences or mathematics library, and it would be an excellent choice for a wide variety of classroom uses." John E. Angus, Technometrics
Dewey Edition20
IllustratedYes
Dewey Decimal519.5/5
Table Of ContentList of figures; Acknowledgement; Preface; Notation and conventions; List of abbreviations; 1. Introduction; 2. Univariate time series models; 3. State space models and the Kalman filter; 4. Estimation, prediction and smoothing for univariate structural time series models; 5. Testing and model selection; 6. Extensions of the univariate model; 7. Explanatory variables; 8. Multivariate models; 9. Continuous time; Appendices; Selected answers to exercises; References; Author index; Subject index.
SynopsisThis 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., This book is concerned with modelling economic and social time series and with addressing the special problems which the treatment of such series pose. It is unique in its use of Kalman filtering with econometric and time series modelling., 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.
LC Classification NumberQA280 .H38 1990
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