State Space Modeling of Time Series

Masanao Aoki

ISBN 10: 3540528709 / ISBN 13: 9783540528708

Edité par Springer Berlin Heidelberg, 1990

A propos de cet article

Druck auf Anfrage Neuware - Printed after ordering - 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. Das vorliegende Buch liefert eine neue, computer-orientierte Methode zur Modellierung von Zeitreihen. Für die neue Auflage wurde es vollständig überarbeitet.

N° de réf. du vendeur 9783540528708

Détails bibliographiques

• Titre: State Space Modeling of Time Series
• Éditeur: Springer Berlin Heidelberg
• Date de parution: 1990
• Langue: anglais
• ISBN à 10 chiffres: 3540528709
• ISBN à 13 chiffres: 9783540528708
• Reliure: Taschenbuch
• Édition: 2ème Édition
• État de l'article: Neu
• Dimensions: 242 L x 170 H x 19 P (en millimètres) 242 millimètres de largeur par 170 millimètres de hauteur par 19 millimètres de profondeur
• Poids: 597 grammes

A propos de ce titre

Synopsis

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.