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Sinopsis
The author adopts a state space approach to time series modelling in this volume to provide a new, computer-orientated method for building models for vector-valued time series. Background material leading up to the two types of estimators of the state space models is collected and presented coherently in four consecutive chapters. This edition has been revised to provide more comprehensive descriptions of state space models for autoregressive models commonly used in the econometric and statistical literature. Backward innovation models are introduced in addition to the forward innovation models, and both are used to construct instrumental variable estimators for the model matrices. Further items in this edition include statistical properties of these two types of estimators, more details on multiplier analysis and the identification of structural models using estimated models, incorporation of exogenous signals and choice of model size. A chapter is devoted to the modelling of integrated, nearly integrated and co-integrated time series.
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Reseña del editor
In this book, the author adopts a state space approach to time series modeling to provide a new, computer-oriented method for building models for vector-valued time series. This second edition has been completely reorganized and rewritten. Background material leading up to the two types of estimators of the state space models is collected and presented coherently in four consecutive chapters. New, fuller descriptions are given of state space models for autoregressive models commonly used in the econometric and statistical literature. Backward innovation models are newly introduced in this edition in addition to the forward innovation models, and both are used to construct instrumental variable estimators for the model matrices. Further new items in this edition include statistical properties of the two types of estimators, more details on multiplier analysis and identification of structural models using estimated models, incorporation of exogenous signals and choice of model size. A whole new chapter is devoted to modeling of integrated, nearly integrated and co-integrated time series.
Reseña del editor
The author adopts a state space approach to time series modelling in this volume to provide a new, computer-orientated method for building models for vector-valued time series. Background material leading up to the two types of estimators of the state space models is collected and presented coherently in four consecutive chapters. This edition has been revised to provide more comprehensive descriptions of state space models for autoregressive models commonly used in the econometric and statistical literature. Backward innovation models are introduced in addition to the forward innovation models, and both are used to construct instrumental variable estimators for the model matrices. Further items in this edition include statistical properties of these two types of estimators, more details on multiplier analysis and the identification of structural models using estimated models, incorporation of exogenous signals and choice of model size. A chapter is devoted to the modelling of integrated, nearly integrated and co-integrated time series.
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State Space Modeling of Time Series (Universitext)
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Springer 01.08.1990., 1990
ISBN 10: 3540528695
ISBN 13: 9783540528692
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Librería: NEPO UG, Rüsselsheim am Main, Alemania
Calificación del vendedor: 5 de 5 estrellas
gebundene Ausgabe. Condición: Gut. Auflage: 2nd, rev. and enlarged ed. 323 Seiten Exemplar aus einer wissenchaftlichen Bibliothek Sprache: Englisch Gewicht in Gramm: 550. Nº de ref. del artículo: 344522
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