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Synopsis
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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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).
Synopsis
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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gebundene Ausgabe. Condition: Gut. Auflage: 2nd, rev. and enlarged ed. 323 Seiten Exemplar aus einer wissenchaftlichen Bibliothek Sprache: Englisch Gewicht in Gramm: 550. Seller Inventory # 344522
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