State Space Modeling of Time Series

Masanao Aoki

ISBN 10: 3540528709 / ISBN 13: 9783540528708

Published by Springer, Springer Vieweg, 1990

About this Item

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.; 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.

Seller Inventory # 9783540528708

Bibliographic Details

• Title: State Space Modeling of Time Series
• Publisher: Springer, Springer Vieweg
• Publication Date: 1990
• Language: English
• ISBN 10: 3540528709
• ISBN 13: 9783540528708
• Binding: Taschenbuch
• Edition: 2nd Edition
• Condition: Neu
• Dimensions: 242W x 170H x 19D millimeters 242 millimeters width by 170 millimeters height by 19 millimeters depth
• Weight: 597 grams

About this title

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.

About the Author

Masanao Aoki is Professor Emeritus in the Department of Economics at the University of California, Los Angeles. He also held professorial appointments at the Institute for Social and Economic Research at Osaka University, Tokyo Institute of Technology, and the University of Illinois. Professor Aoki is a past President of the Society for Economic Dynamics and Control, a Fellow of the Econometric Society, and a Fellow of the IEEE Control Systems Society. Currently Associate Editor of the journal Macroeconomic Dynamics published by Cambridge University Press, he also served as Editor of the Journal of Economic Dynamics and Control and the International Economic Review, and Associate Editor of the IEEE's Transaction of Automatic Control, Information Sciences, and the Journal of Mathematical Analysis and Application. Professor Aoki is the author or editor of ten books, including New Approaches to Macroeconomic Modeling: Evolutionary Stochastic Dynamics, Multiple Equilibria, and Externalities as Field Effects (Cambridge University Press, 1996).