Multiple Time Series Models

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ISBN: 9781412906562 | 1412906563
Cover: Paperback
Copyright: 9/21/2006

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Multiple Time Series Models introduces researchers and students to the different approaches to modeling multivariate time series data including simultaneous equations, ARIMA, error correction models, and vector autoregression. Authors Patrick T. Brandt and John T. Williams focus on vector autoregression (VAR) models as a generalization of these other approaches and discuss specification, estimation, and inference using these models.

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List of Figures
List of Tables
Series Editor?s Introduction
Preface
Introduction to Multiple Time Series Models
Simultaneous Equation Approach
ARIMA Approach
Error Correction or LSE Approach
Vector Autoregression Approach
Comparison and Summary
Basic Vector Autoregression Models
Dynamic Structural Equation Models
Reduced Form Vector Autoregressions
Relationship of a Dynamic Structural Equation Model to a Vector Autoregression Model
Working With This Model
Specification and Analysis of VAR Models
Other Specification Issues
Unit Roots and Error Correction in VARs
Criticisms of VAR
Examples of VAR Analyses
Public Mood and Macropartisanship
Effective Corporate Tax Rates
Conclusion
Appendix: Software for Multiple Time Series Models
Notes
References
Index
About the Authors
Table of Contents provided by Ingram. All Rights Reserved.

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Multiple Time Series Models

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Multiple Time Series Models

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