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- About this Textbook
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This reference work and graduate level textbook considers a wide range of models and methods for analyzing and forecasting multiple time series. The models covered include vector autoregressive, cointegrated,vector autoregressive moving average, multivariate ARCH and periodic processes as well as dynamic simultaneous equations and state space models. Least squares, maximum likelihood and Bayesian methods are considered for estimating these models. Different procedures for model selection and model specification are treated and a wide range of tests and criteria for model checking are introduced. Causality analysis, impulse response analysis and innovation accounting are presented as tools for structural analysis. The book is accessible to graduate students in business and economics. In addition, multiple time series courses in other fields such as statistics and engineering may be based on it. Applied researchers involved in analyzing multiple time series may benefit from the book as it provides the background and tools for their tasks. It bridges the gap to the difficult technical literature on the topic.
- Table of contents (18 chapters)
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Introduction
Pages 1-7
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Stable Vector Autoregressive Processes
Pages 13-68
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Estimation of Vector Autoregressive Processes
Pages 69-133
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VAR Order Selection and Checking the Model Adequacy
Pages 135-192
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VAR Processes with Parameter Constraints
Pages 193-231
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Table of contents (18 chapters)
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- Download Sample pages 1 PDF (438.7 KB)
- Download Table of contents PDF (104.3 KB)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- New Introduction to Multiple Time Series Analysis
- Authors
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- Helmut Lütkepohl
- Copyright
- 2005
- Publisher
- Springer-Verlag Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
- eBook ISBN
- 978-3-540-27752-1
- DOI
- 10.1007/978-3-540-27752-1
- Hardcover ISBN
- 978-3-540-40172-8
- Softcover ISBN
- 978-3-540-26239-8
- Edition Number
- 1
- Number of Pages
- XXI, 764
- Topics
