New introduction to multiple time series analysis
著者
書誌事項
New introduction to multiple time series analysis
Springer, 2007, c2006
Corr. 2nd print
- : pbk
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注記
"With 49 figures and 36 tables"--T.p
Includes bibliographical references (p. [713]-732) and indexes
内容説明・目次
内容説明
This is the new and totally revised edition of Lutkepohl's classic 1991 work. It provides a detailed introduction to the main steps of analyzing multiple time series, model specification, estimation, model checking, and for using the models for economic analysis and forecasting. The book now includes new chapters on cointegration analysis, structural vector autoregressions, cointegrated VARMA processes and multivariate ARCH models. The book bridges the gap to the difficult technical literature on the topic. It 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.
目次
Introduction.- Finite Order Vector Autoregressive Processes: Stable Vector Autoregressive Processes.- Estimation of Vector Autoregressive Processes.- VAR Order Selection and Checking the Model Adequacy.- VAR Processes with Parameter Constraints. Cointegrated Processes: Vector Error Correction Models.- Estimation of Vector Error Correction Models.- Specification of VECMs. Structural and Conditional Models: Structural VARs and VECMs.- Systems of Dynamic Simultaneous Equations. Infinite Order Vector Autoregressive Processes: Vector Autoregressive Moving Average Processes.- Estimation of VARMA Models.- Specification and Checking the Adequacy of VARMA.- Cointegrated VARMA Processes.- Fitting Finite Order VAR Models to Infinite Order Processes. Time Series Topics: Multivariate ARCH and GARCH Models.- Periodic VAR Processes and Intervention Models.- State Space Models. Appendices: Vectors and Matrices.- Multivariate Normal and Related Distributions.- Stochastic Convergence and Asymptotic Distributions.- Evaluating Properties of Estimators and Test Statistics by Simulation and Resampling Techniques.
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