| 书目名称 | State Space Modeling of Time Series | | 编辑 | Masanao Aoki | | 视频video | | | 丛书名称 | Universitext | | 图书封面 |  | | 描述 | 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. | | 出版日期 | Book 1990Latest edition | | 关键词 | Estimator; Instrumentalvariablen; Likelihood; Regression; Regression analysis; Signal; Time series; algorit | | 版次 | 2 | | doi | https://doi.org/10.1007/978-3-642-75883-6 | | isbn_softcover | 978-3-540-52870-8 | | isbn_ebook | 978-3-642-75883-6Series ISSN 0172-5939 Series E-ISSN 2191-6675 | | issn_series | 0172-5939 | | copyright | Springer-Verlag Berlin Heidelberg 1990 |
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发表于 2025-3-21 17:59:27
